Overview

Brought to you by YData

Dataset statistics

Number of variables48
Number of observations897880
Missing cells9099855
Missing cells (%)21.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory328.8 MiB
Average record size in memory384.0 B

Variable types

Text3
Boolean9
DateTime2
Numeric15
Categorical19

Alerts

INJURIES_UNKNOWN has constant value "0.0" Constant
BEAT_OF_OCCURRENCE is highly overall correlated with LATITUDE and 1 other fieldsHigh correlation
CRASH_DATE_EST_I is highly overall correlated with LANE_CNT and 1 other fieldsHigh correlation
CRASH_TYPE is highly overall correlated with MOST_SEVERE_INJURYHigh correlation
DOORING_I is highly overall correlated with FIRST_CRASH_TYPE and 4 other fieldsHigh correlation
FIRST_CRASH_TYPE is highly overall correlated with DOORING_IHigh correlation
HIT_AND_RUN_I is highly overall correlated with STREET_NOHigh correlation
INJURIES_NON_INCAPACITATING is highly overall correlated with INJURIES_TOTALHigh correlation
INJURIES_REPORTED_NOT_EVIDENT is highly overall correlated with INJURIES_TOTALHigh correlation
INJURIES_TOTAL is highly overall correlated with INJURIES_NON_INCAPACITATING and 1 other fieldsHigh correlation
INTERSECTION_RELATED_I is highly overall correlated with STREET_NOHigh correlation
LANE_CNT is highly overall correlated with CRASH_DATE_EST_I and 7 other fieldsHigh correlation
LATITUDE is highly overall correlated with BEAT_OF_OCCURRENCE and 7 other fieldsHigh correlation
LONGITUDE is highly overall correlated with BEAT_OF_OCCURRENCE and 7 other fieldsHigh correlation
MOST_SEVERE_INJURY is highly overall correlated with CRASH_TYPEHigh correlation
NOT_RIGHT_OF_WAY_I is highly overall correlated with LANE_CNT and 3 other fieldsHigh correlation
PHOTOS_TAKEN_I is highly overall correlated with LANE_CNT and 4 other fieldsHigh correlation
ROADWAY_SURFACE_COND is highly overall correlated with WEATHER_CONDITIONHigh correlation
STATEMENTS_TAKEN_I is highly overall correlated with LANE_CNT and 4 other fieldsHigh correlation
STREET_NO is highly overall correlated with CRASH_DATE_EST_I and 9 other fieldsHigh correlation
WEATHER_CONDITION is highly overall correlated with ROADWAY_SURFACE_CONDHigh correlation
WORKERS_PRESENT_I is highly overall correlated with LANE_CNT and 4 other fieldsHigh correlation
WORK_ZONE_I is highly overall correlated with LANE_CNT and 5 other fieldsHigh correlation
WORK_ZONE_TYPE is highly overall correlated with LANE_CNT and 4 other fieldsHigh correlation
TRAFFIC_CONTROL_DEVICE is highly imbalanced (61.5%) Imbalance
DEVICE_CONDITION is highly imbalanced (54.1%) Imbalance
WEATHER_CONDITION is highly imbalanced (66.6%) Imbalance
ALIGNMENT is highly imbalanced (92.2%) Imbalance
ROADWAY_SURFACE_COND is highly imbalanced (55.7%) Imbalance
ROAD_DEFECT is highly imbalanced (69.4%) Imbalance
INTERSECTION_RELATED_I is highly imbalanced (72.3%) Imbalance
NOT_RIGHT_OF_WAY_I is highly imbalanced (55.8%) Imbalance
HIT_AND_RUN_I is highly imbalanced (74.5%) Imbalance
SEC_CONTRIBUTORY_CAUSE is highly imbalanced (54.3%) Imbalance
MOST_SEVERE_INJURY is highly imbalanced (66.2%) Imbalance
INJURIES_FATAL is highly imbalanced (99.4%) Imbalance
CRASH_DATE_EST_I has 831589 (92.6%) missing values Missing
LANE_CNT has 698859 (77.8%) missing values Missing
REPORT_TYPE has 27878 (3.1%) missing values Missing
INTERSECTION_RELATED_I has 691701 (77.0%) missing values Missing
NOT_RIGHT_OF_WAY_I has 856988 (95.4%) missing values Missing
HIT_AND_RUN_I has 616240 (68.6%) missing values Missing
PHOTOS_TAKEN_I has 885622 (98.6%) missing values Missing
STATEMENTS_TAKEN_I has 877235 (97.7%) missing values Missing
DOORING_I has 895036 (99.7%) missing values Missing
WORK_ZONE_I has 892860 (99.4%) missing values Missing
WORK_ZONE_TYPE has 894001 (99.6%) missing values Missing
WORKERS_PRESENT_I has 896584 (99.9%) missing values Missing
LANE_CNT is highly skewed (γ1 = 350.3141581) Skewed
LATITUDE is highly skewed (γ1 = -116.0882465) Skewed
LONGITUDE is highly skewed (γ1 = 127.0417048) Skewed
CRASH_RECORD_ID has unique values Unique
INJURIES_TOTAL has 769772 (85.7%) zeros Zeros
INJURIES_INCAPACITATING has 880718 (98.1%) zeros Zeros
INJURIES_NON_INCAPACITATING has 822306 (91.6%) zeros Zeros
INJURIES_REPORTED_NOT_EVIDENT has 852119 (94.9%) zeros Zeros
INJURIES_NO_INDICATION has 19237 (2.1%) zeros Zeros
CRASH_HOUR has 19577 (2.2%) zeros Zeros

Reproduction

Analysis started2024-12-04 17:40:41.003880
Analysis finished2024-12-04 17:58:01.006600
Duration17 minutes and 20 seconds
Software versionydata-profiling vv4.12.0
Download configurationconfig.json

Variables

CRASH_RECORD_ID
Text

Unique 

Distinct897880
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size6.9 MiB
2024-12-04T12:58:01.771560image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Length

Max length128
Median length128
Mean length128
Min length128

Characters and Unicode

Total characters114928640
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique897880 ?
Unique (%)100.0%

Sample

1st row23a79931ef555d54118f64dc9be2cf2dbf59636ce253f7a1179c4a1c091442a6eeab8352220c7c56ca1ff7c4b4b0fc345c74e3e85ecb9d43deeb66b5f803d4a0
2nd row2675c13fd0f474d730a5b780968b3cafc7c12d7adb661fa8a3093c0658d5a0d51b720fc9e031a1ddd83c761a8e2aa7283573557db246f4c9e956aaa58719cacf
3rd row5f54a59fcb087b12ae5b1acff96a3caf4f2d37e79f8db4106558b34b8a6d2b81af02cf91b576ecd7ced08ffd10fcfd940a84f7613125b89d33636e6075064e22
4th row7ebf015016f83d09b321afd671a836d6b148330535d5df85f232edb575a7f2a42e61b9747067e89c4e7a73e69efc819c9003ed153e19765f2ecc6f7b2421c98d
5th row6c1659069e9c6285a650e70d6f9b574ed5f64c12888479093dfeef179c0344ec6d2057eae224b5c0d5dfc278c0a237f8c22543f07fdef2e4a95a3849871c9345
ValueCountFrequency (%)
569af2dc0eaa9c5a55a01f1d5fd678644c3dd76d586b51f7b87b1b5c6c3cf8700b10a9fea3c98cfc8df295c8b4db1fe28c4209e34e3a021b50c56023c798d1f9 1
 
< 0.1%
2ee6209bde600a6ae2f12fb385b1e5749803cc01d0e954d5016091ecb13f424d48e097f71fa5d95741f5870f7d3a76d9189293c77411b3b92c925d26239872b3 1
 
< 0.1%
23a79931ef555d54118f64dc9be2cf2dbf59636ce253f7a1179c4a1c091442a6eeab8352220c7c56ca1ff7c4b4b0fc345c74e3e85ecb9d43deeb66b5f803d4a0 1
 
< 0.1%
2675c13fd0f474d730a5b780968b3cafc7c12d7adb661fa8a3093c0658d5a0d51b720fc9e031a1ddd83c761a8e2aa7283573557db246f4c9e956aaa58719cacf 1
 
< 0.1%
5f54a59fcb087b12ae5b1acff96a3caf4f2d37e79f8db4106558b34b8a6d2b81af02cf91b576ecd7ced08ffd10fcfd940a84f7613125b89d33636e6075064e22 1
 
< 0.1%
7ebf015016f83d09b321afd671a836d6b148330535d5df85f232edb575a7f2a42e61b9747067e89c4e7a73e69efc819c9003ed153e19765f2ecc6f7b2421c98d 1
 
< 0.1%
6c1659069e9c6285a650e70d6f9b574ed5f64c12888479093dfeef179c0344ec6d2057eae224b5c0d5dfc278c0a237f8c22543f07fdef2e4a95a3849871c9345 1
 
< 0.1%
004cd14d0303a9163aad69a2d7f341b7da2a8572b2ab3378594bfae8ac53dcb604dd8d414f93c290b55862f9f2517ad32e6209cbc8034c2e26eb3c2bc9724390 1
 
< 0.1%
35156ce97cab22747495e92e8bbb16c57e0e60dc3ce6d1f1852f2f7cece07c7ae825b073b286b1da52dfa58082ff6d763ecf1f13f06a223c7aed2b6c1e8c5972 1
 
< 0.1%
359bf9f5872d646bb63576e55b1e0b480dc93c2b935ab571dc26ddb48b7a328fbfe130ae70bbff9f03787041b6fb029ba02529da9a1f57494e385ec0e13ed834 1
 
< 0.1%
Other values (897870) 897870
> 99.9%
2024-12-04T12:58:02.827560image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 7188359
 
6.3%
b 7188345
 
6.3%
f 7187062
 
6.3%
d 7186483
 
6.3%
c 7185916
 
6.3%
4 7184239
 
6.3%
9 7183695
 
6.3%
8 7183022
 
6.2%
0 7182922
 
6.2%
7 7181759
 
6.2%
Other values (6) 43076838
37.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 114928640
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 7188359
 
6.3%
b 7188345
 
6.3%
f 7187062
 
6.3%
d 7186483
 
6.3%
c 7185916
 
6.3%
4 7184239
 
6.3%
9 7183695
 
6.3%
8 7183022
 
6.2%
0 7182922
 
6.2%
7 7181759
 
6.2%
Other values (6) 43076838
37.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 114928640
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 7188359
 
6.3%
b 7188345
 
6.3%
f 7187062
 
6.3%
d 7186483
 
6.3%
c 7185916
 
6.3%
4 7184239
 
6.3%
9 7183695
 
6.3%
8 7183022
 
6.2%
0 7182922
 
6.2%
7 7181759
 
6.2%
Other values (6) 43076838
37.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 114928640
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 7188359
 
6.3%
b 7188345
 
6.3%
f 7187062
 
6.3%
d 7186483
 
6.3%
c 7185916
 
6.3%
4 7184239
 
6.3%
9 7183695
 
6.3%
8 7183022
 
6.2%
0 7182922
 
6.2%
7 7181759
 
6.2%
Other values (6) 43076838
37.5%

CRASH_DATE_EST_I
Boolean

High correlation  Missing 

Distinct2
Distinct (%)< 0.1%
Missing831589
Missing (%)92.6%
Memory size1.7 MiB
True
 
57777
False
 
8514
(Missing)
831589 
ValueCountFrequency (%)
True 57777
 
6.4%
False 8514
 
0.9%
(Missing) 831589
92.6%
2024-12-04T12:58:02.941391image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Distinct590438
Distinct (%)65.8%
Missing0
Missing (%)0.0%
Memory size6.9 MiB
Minimum2013-03-03 16:48:00
Maximum2024-12-02 02:45:00
2024-12-04T12:58:03.069393image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:58:03.227607image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

POSTED_SPEED_LIMIT
Real number (ℝ)

Distinct46
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean28.422203
Minimum0
Maximum99
Zeros7579
Zeros (%)0.8%
Negative0
Negative (%)0.0%
Memory size6.9 MiB
2024-12-04T12:58:03.391607image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile15
Q130
median30
Q330
95-th percentile35
Maximum99
Range99
Interquartile range (IQR)0

Descriptive statistics

Standard deviation6.1073021
Coefficient of variation (CV)0.21487786
Kurtosis7.5701571
Mean28.422203
Median Absolute Deviation (MAD)0
Skewness-1.8145501
Sum25519728
Variance37.299139
MonotonicityNot monotonic
2024-12-04T12:58:03.537609image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram with fixed size bins (bins=46)
ValueCountFrequency (%)
30 661405
73.7%
35 59467
 
6.6%
25 57503
 
6.4%
20 37538
 
4.2%
15 31977
 
3.6%
10 21003
 
2.3%
40 8583
 
1.0%
0 7579
 
0.8%
45 5932
 
0.7%
5 4941
 
0.6%
Other values (36) 1952
 
0.2%
ValueCountFrequency (%)
0 7579
0.8%
1 41
 
< 0.1%
2 31
 
< 0.1%
3 219
 
< 0.1%
4 2
 
< 0.1%
5 4941
0.6%
6 7
 
< 0.1%
7 6
 
< 0.1%
8 2
 
< 0.1%
9 96
 
< 0.1%
ValueCountFrequency (%)
99 66
 
< 0.1%
70 7
 
< 0.1%
65 20
 
< 0.1%
63 1
 
< 0.1%
62 1
 
< 0.1%
60 53
 
< 0.1%
55 880
0.1%
50 276
 
< 0.1%
49 1
 
< 0.1%
46 1
 
< 0.1%

TRAFFIC_CONTROL_DEVICE
Categorical

Imbalance 

Distinct19
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.9 MiB
NO CONTROLS
508327 
TRAFFIC SIGNAL
248905 
STOP SIGN/FLASHER
89002 
UNKNOWN
 
38109
OTHER
 
6075
Other values (14)
 
7462

Length

Max length24
Median length11
Mean length12.250412
Min length5

Characters and Unicode

Total characters10999400
Distinct characters25
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTRAFFIC SIGNAL
2nd rowNO CONTROLS
3rd rowTRAFFIC SIGNAL
4th rowNO CONTROLS
5th rowOTHER

Common Values

ValueCountFrequency (%)
NO CONTROLS 508327
56.6%
TRAFFIC SIGNAL 248905
27.7%
STOP SIGN/FLASHER 89002
 
9.9%
UNKNOWN 38109
 
4.2%
OTHER 6075
 
0.7%
YIELD 1359
 
0.2%
LANE USE MARKING 1226
 
0.1%
OTHER REG. SIGN 1093
 
0.1%
OTHER WARNING SIGN 711
 
0.1%
PEDESTRIAN CROSSING SIGN 634
 
0.1%
Other values (9) 2439
 
0.3%

Length

2024-12-04T12:58:03.682611image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
no 508385
29.0%
controls 508327
29.0%
signal 249276
14.2%
traffic 248905
14.2%
stop 89002
 
5.1%
sign/flasher 89002
 
5.1%
unknown 38109
 
2.2%
other 8071
 
0.5%
sign 2666
 
0.2%
crossing 1634
 
0.1%
Other values (18) 11215
 
0.6%

Most occurring characters

ValueCountFrequency (%)
O 1665079
15.1%
N 1479935
13.5%
S 1033592
9.4%
R 862258
7.8%
856712
7.8%
T 856242
7.8%
L 852056
7.7%
C 759963
6.9%
I 597308
 
5.4%
A 594501
 
5.4%
Other values (15) 1441754
13.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 10999400
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
O 1665079
15.1%
N 1479935
13.5%
S 1033592
9.4%
R 862258
7.8%
856712
7.8%
T 856242
7.8%
L 852056
7.7%
C 759963
6.9%
I 597308
 
5.4%
A 594501
 
5.4%
Other values (15) 1441754
13.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 10999400
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
O 1665079
15.1%
N 1479935
13.5%
S 1033592
9.4%
R 862258
7.8%
856712
7.8%
T 856242
7.8%
L 852056
7.7%
C 759963
6.9%
I 597308
 
5.4%
A 594501
 
5.4%
Other values (15) 1441754
13.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 10999400
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
O 1665079
15.1%
N 1479935
13.5%
S 1033592
9.4%
R 862258
7.8%
856712
7.8%
T 856242
7.8%
L 852056
7.7%
C 759963
6.9%
I 597308
 
5.4%
A 594501
 
5.4%
Other values (15) 1441754
13.1%

DEVICE_CONDITION
Categorical

Imbalance 

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.9 MiB
NO CONTROLS
514347 
FUNCTIONING PROPERLY
306582 
UNKNOWN
63082 
OTHER
 
6812
FUNCTIONING IMPROPERLY
 
4106
Other values (3)
 
2951

Length

Max length24
Median length11
Mean length13.812026
Min length5

Characters and Unicode

Total characters12401542
Distinct characters21
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFUNCTIONING PROPERLY
2nd rowNO CONTROLS
3rd rowFUNCTIONING PROPERLY
4th rowNO CONTROLS
5th rowFUNCTIONING PROPERLY

Common Values

ValueCountFrequency (%)
NO CONTROLS 514347
57.3%
FUNCTIONING PROPERLY 306582
34.1%
UNKNOWN 63082
 
7.0%
OTHER 6812
 
0.8%
FUNCTIONING IMPROPERLY 4106
 
0.5%
NOT FUNCTIONING 2558
 
0.3%
WORN REFLECTIVE MATERIAL 294
 
< 0.1%
MISSING 99
 
< 0.1%

Length

2024-12-04T12:58:03.819620image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-04T12:58:03.945610image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
ValueCountFrequency (%)
no 514347
29.8%
controls 514347
29.8%
functioning 313246
18.1%
properly 306582
17.8%
unknown 63082
 
3.7%
other 6812
 
0.4%
improperly 4106
 
0.2%
not 2558
 
0.1%
worn 294
 
< 0.1%
reflective 294
 
< 0.1%
Other values (2) 393
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
O 2239721
18.1%
N 2160629
17.4%
R 1143417
9.2%
T 837551
 
6.8%
828181
 
6.7%
C 827887
 
6.7%
L 825623
 
6.7%
I 631384
 
5.1%
P 621376
 
5.0%
S 514545
 
4.1%
Other values (11) 1771228
14.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 12401542
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
O 2239721
18.1%
N 2160629
17.4%
R 1143417
9.2%
T 837551
 
6.8%
828181
 
6.7%
C 827887
 
6.7%
L 825623
 
6.7%
I 631384
 
5.1%
P 621376
 
5.0%
S 514545
 
4.1%
Other values (11) 1771228
14.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 12401542
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
O 2239721
18.1%
N 2160629
17.4%
R 1143417
9.2%
T 837551
 
6.8%
828181
 
6.7%
C 827887
 
6.7%
L 825623
 
6.7%
I 631384
 
5.1%
P 621376
 
5.0%
S 514545
 
4.1%
Other values (11) 1771228
14.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 12401542
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
O 2239721
18.1%
N 2160629
17.4%
R 1143417
9.2%
T 837551
 
6.8%
828181
 
6.7%
C 827887
 
6.7%
L 825623
 
6.7%
I 631384
 
5.1%
P 621376
 
5.0%
S 514545
 
4.1%
Other values (11) 1771228
14.3%

WEATHER_CONDITION
Categorical

High correlation  Imbalance 

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.9 MiB
CLEAR
706141 
RAIN
77938 
UNKNOWN
 
51207
SNOW
 
28818
CLOUDY/OVERCAST
 
26223
Other values (7)
 
7553

Length

Max length24
Median length5
Mean length5.3448991
Min length4

Characters and Unicode

Total characters4799078
Distinct characters25
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCLEAR
2nd rowCLEAR
3rd rowCLEAR
4th rowCLEAR
5th rowCLEAR

Common Values

ValueCountFrequency (%)
CLEAR 706141
78.6%
RAIN 77938
 
8.7%
UNKNOWN 51207
 
5.7%
SNOW 28818
 
3.2%
CLOUDY/OVERCAST 26223
 
2.9%
OTHER 2778
 
0.3%
FREEZING RAIN/DRIZZLE 1785
 
0.2%
FOG/SMOKE/HAZE 1352
 
0.2%
SLEET/HAIL 1024
 
0.1%
BLOWING SNOW 451
 
0.1%
Other values (2) 163
 
< 0.1%

Length

2024-12-04T12:58:04.098608image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
clear 706141
78.4%
rain 77938
 
8.7%
unknown 51207
 
5.7%
snow 29269
 
3.2%
cloudy/overcast 26223
 
2.9%
other 2778
 
0.3%
freezing 1785
 
0.2%
rain/drizzle 1785
 
0.2%
fog/smoke/haze 1352
 
0.2%
sleet/hail 1024
 
0.1%
Other values (8) 1103
 
0.1%

Most occurring characters

ValueCountFrequency (%)
R 818754
17.1%
A 814626
17.0%
C 758743
15.8%
E 745873
15.5%
L 736662
15.4%
N 265019
 
5.5%
O 139025
 
2.9%
I 84945
 
1.8%
W 81090
 
1.7%
U 77430
 
1.6%
Other values (15) 276911
 
5.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4799078
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
R 818754
17.1%
A 814626
17.0%
C 758743
15.8%
E 745873
15.5%
L 736662
15.4%
N 265019
 
5.5%
O 139025
 
2.9%
I 84945
 
1.8%
W 81090
 
1.7%
U 77430
 
1.6%
Other values (15) 276911
 
5.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4799078
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
R 818754
17.1%
A 814626
17.0%
C 758743
15.8%
E 745873
15.5%
L 736662
15.4%
N 265019
 
5.5%
O 139025
 
2.9%
I 84945
 
1.8%
W 81090
 
1.7%
U 77430
 
1.6%
Other values (15) 276911
 
5.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4799078
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
R 818754
17.1%
A 814626
17.0%
C 758743
15.8%
E 745873
15.5%
L 736662
15.4%
N 265019
 
5.5%
O 139025
 
2.9%
I 84945
 
1.8%
W 81090
 
1.7%
U 77430
 
1.6%
Other values (15) 276911
 
5.8%
Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.9 MiB
DAYLIGHT
576641 
DARKNESS, LIGHTED ROAD
196105 
UNKNOWN
 
42325
DARKNESS
 
42225
DUSK
 
25613

Length

Max length22
Median length8
Mean length10.829787
Min length4

Characters and Unicode

Total characters9723849
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDUSK
2nd rowDARKNESS, LIGHTED ROAD
3rd rowDAYLIGHT
4th rowDARKNESS, LIGHTED ROAD
5th rowDAYLIGHT

Common Values

ValueCountFrequency (%)
DAYLIGHT 576641
64.2%
DARKNESS, LIGHTED ROAD 196105
 
21.8%
UNKNOWN 42325
 
4.7%
DARKNESS 42225
 
4.7%
DUSK 25613
 
2.9%
DAWN 14971
 
1.7%

Length

2024-12-04T12:58:04.235610image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-04T12:58:04.357619image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
ValueCountFrequency (%)
daylight 576641
44.7%
darkness 238330
18.5%
lighted 196105
 
15.2%
road 196105
 
15.2%
unknown 42325
 
3.3%
dusk 25613
 
2.0%
dawn 14971
 
1.2%

Most occurring characters

ValueCountFrequency (%)
D 1247765
12.8%
A 1026047
10.6%
L 772746
 
7.9%
I 772746
 
7.9%
G 772746
 
7.9%
H 772746
 
7.9%
T 772746
 
7.9%
Y 576641
 
5.9%
S 502273
 
5.2%
R 434435
 
4.5%
Other values (8) 2072958
21.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 9723849
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
D 1247765
12.8%
A 1026047
10.6%
L 772746
 
7.9%
I 772746
 
7.9%
G 772746
 
7.9%
H 772746
 
7.9%
T 772746
 
7.9%
Y 576641
 
5.9%
S 502273
 
5.2%
R 434435
 
4.5%
Other values (8) 2072958
21.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 9723849
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
D 1247765
12.8%
A 1026047
10.6%
L 772746
 
7.9%
I 772746
 
7.9%
G 772746
 
7.9%
H 772746
 
7.9%
T 772746
 
7.9%
Y 576641
 
5.9%
S 502273
 
5.2%
R 434435
 
4.5%
Other values (8) 2072958
21.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 9723849
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
D 1247765
12.8%
A 1026047
10.6%
L 772746
 
7.9%
I 772746
 
7.9%
G 772746
 
7.9%
H 772746
 
7.9%
T 772746
 
7.9%
Y 576641
 
5.9%
S 502273
 
5.2%
R 434435
 
4.5%
Other values (8) 2072958
21.3%

FIRST_CRASH_TYPE
Categorical

High correlation 

Distinct18
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.9 MiB
PARKED MOTOR VEHICLE
207908 
REAR END
198669 
SIDESWIPE SAME DIRECTION
137819 
TURNING
129084 
ANGLE
97591 
Other values (13)
126809 

Length

Max length28
Median length20
Mean length13.484839
Min length5

Characters and Unicode

Total characters12107767
Distinct characters25
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowANGLE
2nd rowREAR END
3rd rowPARKED MOTOR VEHICLE
4th rowSIDESWIPE SAME DIRECTION
5th rowREAR END

Common Values

ValueCountFrequency (%)
PARKED MOTOR VEHICLE 207908
23.2%
REAR END 198669
22.1%
SIDESWIPE SAME DIRECTION 137819
15.3%
TURNING 129084
14.4%
ANGLE 97591
10.9%
FIXED OBJECT 41714
 
4.6%
PEDESTRIAN 21234
 
2.4%
PEDALCYCLIST 14281
 
1.6%
SIDESWIPE OPPOSITE DIRECTION 12472
 
1.4%
REAR TO FRONT 9195
 
1.0%
Other values (8) 27913
 
3.1%

Length

2024-12-04T12:58:04.518610image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
rear 217136
11.4%
parked 207908
10.9%
motor 207908
10.9%
vehicle 207908
10.9%
end 198669
10.4%
sideswipe 150291
7.9%
direction 150291
7.9%
same 137819
7.2%
turning 129084
6.8%
angle 97591
5.1%
Other values (15) 202496
10.6%

Most occurring characters

ValueCountFrequency (%)
E 1913270
15.8%
R 1172709
9.7%
I 1039511
 
8.6%
1009221
 
8.3%
D 798021
 
6.6%
N 752219
 
6.2%
A 704928
 
5.8%
O 695539
 
5.7%
T 623971
 
5.2%
S 494606
 
4.1%
Other values (15) 2903772
24.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 12107767
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
E 1913270
15.8%
R 1172709
9.7%
I 1039511
 
8.6%
1009221
 
8.3%
D 798021
 
6.6%
N 752219
 
6.2%
A 704928
 
5.8%
O 695539
 
5.7%
T 623971
 
5.2%
S 494606
 
4.1%
Other values (15) 2903772
24.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 12107767
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
E 1913270
15.8%
R 1172709
9.7%
I 1039511
 
8.6%
1009221
 
8.3%
D 798021
 
6.6%
N 752219
 
6.2%
A 704928
 
5.8%
O 695539
 
5.7%
T 623971
 
5.2%
S 494606
 
4.1%
Other values (15) 2903772
24.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 12107767
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
E 1913270
15.8%
R 1172709
9.7%
I 1039511
 
8.6%
1009221
 
8.3%
D 798021
 
6.6%
N 752219
 
6.2%
A 704928
 
5.8%
O 695539
 
5.7%
T 623971
 
5.2%
S 494606
 
4.1%
Other values (15) 2903772
24.0%

TRAFFICWAY_TYPE
Categorical

Distinct20
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.9 MiB
NOT DIVIDED
386853 
DIVIDED - W/MEDIAN (NOT RAISED)
141936 
ONE-WAY
113675 
FOUR WAY
62104 
PARKING LOT
60781 
Other values (15)
132531 

Length

Max length31
Median length26
Mean length14.096014
Min length4

Characters and Unicode

Total characters12656529
Distinct characters28
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFIVE POINT, OR MORE
2nd rowDIVIDED - W/MEDIAN BARRIER
3rd rowDIVIDED - W/MEDIAN (NOT RAISED)
4th rowNOT DIVIDED
5th rowOTHER

Common Values

ValueCountFrequency (%)
NOT DIVIDED 386853
43.1%
DIVIDED - W/MEDIAN (NOT RAISED) 141936
 
15.8%
ONE-WAY 113675
 
12.7%
FOUR WAY 62104
 
6.9%
PARKING LOT 60781
 
6.8%
DIVIDED - W/MEDIAN BARRIER 50773
 
5.7%
OTHER 24296
 
2.7%
ALLEY 14749
 
1.6%
T-INTERSECTION 12324
 
1.4%
UNKNOWN 10558
 
1.2%
Other values (10) 19831
 
2.2%

Length

2024-12-04T12:58:04.637610image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
divided 579562
26.9%
not 529470
24.6%
192709
 
9.0%
w/median 192709
 
9.0%
raised 141936
 
6.6%
one-way 113675
 
5.3%
four 62104
 
2.9%
way 62104
 
2.9%
parking 60781
 
2.8%
lot 60781
 
2.8%
Other values (22) 155994
 
7.2%

Most occurring characters

ValueCountFrequency (%)
D 2077198
16.4%
I 1645291
13.0%
1253945
9.9%
E 1180831
9.3%
N 990477
7.8%
O 826785
 
6.5%
T 680171
 
5.4%
A 650232
 
5.1%
V 583815
 
4.6%
R 483144
 
3.8%
Other values (18) 2284640
18.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 12656529
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
D 2077198
16.4%
I 1645291
13.0%
1253945
9.9%
E 1180831
9.3%
N 990477
7.8%
O 826785
 
6.5%
T 680171
 
5.4%
A 650232
 
5.1%
V 583815
 
4.6%
R 483144
 
3.8%
Other values (18) 2284640
18.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 12656529
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
D 2077198
16.4%
I 1645291
13.0%
1253945
9.9%
E 1180831
9.3%
N 990477
7.8%
O 826785
 
6.5%
T 680171
 
5.4%
A 650232
 
5.1%
V 583815
 
4.6%
R 483144
 
3.8%
Other values (18) 2284640
18.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 12656529
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
D 2077198
16.4%
I 1645291
13.0%
1253945
9.9%
E 1180831
9.3%
N 990477
7.8%
O 826785
 
6.5%
T 680171
 
5.4%
A 650232
 
5.1%
V 583815
 
4.6%
R 483144
 
3.8%
Other values (18) 2284640
18.1%

LANE_CNT
Real number (ℝ)

High correlation  Missing  Skewed 

Distinct41
Distinct (%)< 0.1%
Missing698859
Missing (%)77.8%
Infinite0
Infinite (%)0.0%
Mean13.329468
Minimum0
Maximum1191625
Zeros8032
Zeros (%)0.9%
Negative0
Negative (%)0.0%
Memory size6.9 MiB
2024-12-04T12:58:04.766610image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median2
Q34
95-th percentile4
Maximum1191625
Range1191625
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2961.5119
Coefficient of variation (CV)222.1778
Kurtosis134686.71
Mean13.329468
Median Absolute Deviation (MAD)1
Skewness350.31416
Sum2652844
Variance8770552.5
MonotonicityNot monotonic
2024-12-04T12:58:04.914610image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
2 91162
 
10.2%
4 49588
 
5.5%
1 32550
 
3.6%
3 8678
 
1.0%
0 8032
 
0.9%
6 4502
 
0.5%
5 1940
 
0.2%
8 1908
 
0.2%
7 184
 
< 0.1%
10 162
 
< 0.1%
Other values (31) 315
 
< 0.1%
(Missing) 698859
77.8%
ValueCountFrequency (%)
0 8032
 
0.9%
1 32550
 
3.6%
2 91162
10.2%
3 8678
 
1.0%
4 49588
5.5%
5 1940
 
0.2%
6 4502
 
0.5%
7 184
 
< 0.1%
8 1908
 
0.2%
9 66
 
< 0.1%
ValueCountFrequency (%)
1191625 1
 
< 0.1%
433634 1
 
< 0.1%
299679 1
 
< 0.1%
218474 1
 
< 0.1%
902 1
 
< 0.1%
400 1
 
< 0.1%
100 2
 
< 0.1%
99 108
< 0.1%
80 1
 
< 0.1%
60 3
 
< 0.1%

ALIGNMENT
Categorical

Imbalance 

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.9 MiB
STRAIGHT AND LEVEL
876623 
STRAIGHT ON GRADE
 
10976
CURVE, LEVEL
 
6322
STRAIGHT ON HILLCREST
 
2263
CURVE ON GRADE
 
1308

Length

Max length21
Median length18
Mean length17.947264
Min length12

Characters and Unicode

Total characters16114489
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSTRAIGHT AND LEVEL
2nd rowSTRAIGHT AND LEVEL
3rd rowSTRAIGHT AND LEVEL
4th rowSTRAIGHT AND LEVEL
5th rowSTRAIGHT AND LEVEL

Common Values

ValueCountFrequency (%)
STRAIGHT AND LEVEL 876623
97.6%
STRAIGHT ON GRADE 10976
 
1.2%
CURVE, LEVEL 6322
 
0.7%
STRAIGHT ON HILLCREST 2263
 
0.3%
CURVE ON GRADE 1308
 
0.1%
CURVE ON HILLCREST 388
 
< 0.1%

Length

2024-12-04T12:58:05.041610image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-04T12:58:05.156610image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
ValueCountFrequency (%)
straight 889862
33.1%
level 882945
32.9%
and 876623
32.6%
on 14935
 
0.6%
grade 12284
 
0.5%
curve 8018
 
0.3%
hillcrest 2651
 
0.1%

Most occurring characters

ValueCountFrequency (%)
1789438
11.1%
E 1788843
11.1%
T 1782375
11.1%
A 1778769
11.0%
L 1771192
11.0%
R 912815
 
5.7%
G 902146
 
5.6%
S 892513
 
5.5%
I 892513
 
5.5%
H 892513
 
5.5%
Other values (7) 2711372
16.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 16114489
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1789438
11.1%
E 1788843
11.1%
T 1782375
11.1%
A 1778769
11.0%
L 1771192
11.0%
R 912815
 
5.7%
G 902146
 
5.6%
S 892513
 
5.5%
I 892513
 
5.5%
H 892513
 
5.5%
Other values (7) 2711372
16.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 16114489
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1789438
11.1%
E 1788843
11.1%
T 1782375
11.1%
A 1778769
11.0%
L 1771192
11.0%
R 912815
 
5.7%
G 902146
 
5.6%
S 892513
 
5.5%
I 892513
 
5.5%
H 892513
 
5.5%
Other values (7) 2711372
16.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 16114489
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1789438
11.1%
E 1788843
11.1%
T 1782375
11.1%
A 1778769
11.0%
L 1771192
11.0%
R 912815
 
5.7%
G 902146
 
5.6%
S 892513
 
5.5%
I 892513
 
5.5%
H 892513
 
5.5%
Other values (7) 2711372
16.8%

ROADWAY_SURFACE_COND
Categorical

High correlation  Imbalance 

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.9 MiB
DRY
664331 
WET
117249 
UNKNOWN
79538 
SNOW OR SLUSH
 
28502
ICE
 
5666
Other values (2)
 
2594

Length

Max length15
Median length3
Mean length3.6811378
Min length3

Characters and Unicode

Total characters3305220
Distinct characters19
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDRY
2nd rowDRY
3rd rowDRY
4th rowDRY
5th rowDRY

Common Values

ValueCountFrequency (%)
DRY 664331
74.0%
WET 117249
 
13.1%
UNKNOWN 79538
 
8.9%
SNOW OR SLUSH 28502
 
3.2%
ICE 5666
 
0.6%
OTHER 2272
 
0.3%
SAND, MUD, DIRT 322
 
< 0.1%

Length

2024-12-04T12:58:05.306610image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-04T12:58:05.424610image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
ValueCountFrequency (%)
dry 664331
69.5%
wet 117249
 
12.3%
unknown 79538
 
8.3%
snow 28502
 
3.0%
or 28502
 
3.0%
slush 28502
 
3.0%
ice 5666
 
0.6%
other 2272
 
0.2%
sand 322
 
< 0.1%
mud 322
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
R 695427
21.0%
D 665297
20.1%
Y 664331
20.1%
N 267438
 
8.1%
W 225289
 
6.8%
O 138814
 
4.2%
E 125187
 
3.8%
T 119843
 
3.6%
U 108362
 
3.3%
S 85828
 
2.6%
Other values (9) 209404
 
6.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3305220
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
R 695427
21.0%
D 665297
20.1%
Y 664331
20.1%
N 267438
 
8.1%
W 225289
 
6.8%
O 138814
 
4.2%
E 125187
 
3.8%
T 119843
 
3.6%
U 108362
 
3.3%
S 85828
 
2.6%
Other values (9) 209404
 
6.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3305220
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
R 695427
21.0%
D 665297
20.1%
Y 664331
20.1%
N 267438
 
8.1%
W 225289
 
6.8%
O 138814
 
4.2%
E 125187
 
3.8%
T 119843
 
3.6%
U 108362
 
3.3%
S 85828
 
2.6%
Other values (9) 209404
 
6.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3305220
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
R 695427
21.0%
D 665297
20.1%
Y 664331
20.1%
N 267438
 
8.1%
W 225289
 
6.8%
O 138814
 
4.2%
E 125187
 
3.8%
T 119843
 
3.6%
U 108362
 
3.3%
S 85828
 
2.6%
Other values (9) 209404
 
6.3%

ROAD_DEFECT
Categorical

Imbalance 

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.9 MiB
NO DEFECTS
715503 
UNKNOWN
165231 
RUT, HOLES
 
6344
OTHER
 
4874
WORN SURFACE
 
3722
Other values (2)
 
2206

Length

Max length17
Median length10
Mean length9.4428309
Min length5

Characters and Unicode

Total characters8478529
Distinct characters20
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNO DEFECTS
2nd rowNO DEFECTS
3rd rowNO DEFECTS
4th rowNO DEFECTS
5th rowNO DEFECTS

Common Values

ValueCountFrequency (%)
NO DEFECTS 715503
79.7%
UNKNOWN 165231
 
18.4%
RUT, HOLES 6344
 
0.7%
OTHER 4874
 
0.5%
WORN SURFACE 3722
 
0.4%
SHOULDER DEFECT 1547
 
0.2%
DEBRIS ON ROADWAY 659
 
0.1%

Length

2024-12-04T12:58:05.565610image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-04T12:58:05.692610image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
ValueCountFrequency (%)
no 715503
44.0%
defects 715503
44.0%
unknown 165231
 
10.2%
rut 6344
 
0.4%
holes 6344
 
0.4%
other 4874
 
0.3%
worn 3722
 
0.2%
surface 3722
 
0.2%
shoulder 1547
 
0.1%
defect 1547
 
0.1%
Other values (3) 1977
 
0.1%

Most occurring characters

ValueCountFrequency (%)
E 1451246
17.1%
N 1215577
14.3%
O 898539
10.6%
728434
8.6%
T 728268
8.6%
S 727775
8.6%
C 720772
8.5%
F 720772
8.5%
D 719915
8.5%
U 176844
 
2.1%
Other values (10) 390387
 
4.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 8478529
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
E 1451246
17.1%
N 1215577
14.3%
O 898539
10.6%
728434
8.6%
T 728268
8.6%
S 727775
8.6%
C 720772
8.5%
F 720772
8.5%
D 719915
8.5%
U 176844
 
2.1%
Other values (10) 390387
 
4.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 8478529
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
E 1451246
17.1%
N 1215577
14.3%
O 898539
10.6%
728434
8.6%
T 728268
8.6%
S 727775
8.6%
C 720772
8.5%
F 720772
8.5%
D 719915
8.5%
U 176844
 
2.1%
Other values (10) 390387
 
4.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 8478529
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
E 1451246
17.1%
N 1215577
14.3%
O 898539
10.6%
728434
8.6%
T 728268
8.6%
S 727775
8.6%
C 720772
8.5%
F 720772
8.5%
D 719915
8.5%
U 176844
 
2.1%
Other values (10) 390387
 
4.6%

REPORT_TYPE
Categorical

Missing 

Distinct3
Distinct (%)< 0.1%
Missing27878
Missing (%)3.1%
Memory size6.9 MiB
NOT ON SCENE (DESK REPORT)
488897 
ON SCENE
380865 
AMENDED
 
240

Length

Max length26
Median length26
Mean length18.114811
Min length7

Characters and Unicode

Total characters15759922
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowON SCENE
2nd rowON SCENE
3rd rowON SCENE
4th rowON SCENE
5th rowON SCENE

Common Values

ValueCountFrequency (%)
NOT ON SCENE (DESK REPORT) 488897
54.5%
ON SCENE 380865
42.4%
AMENDED 240
 
< 0.1%
(Missing) 27878
 
3.1%

Length

2024-12-04T12:58:05.840633image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-04T12:58:05.963611image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
ValueCountFrequency (%)
on 869762
27.1%
scene 869762
27.1%
not 488897
15.2%
desk 488897
15.2%
report 488897
15.2%
amended 240
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
E 2717798
17.2%
2336453
14.8%
N 2228661
14.1%
O 1847556
11.7%
S 1358659
8.6%
T 977794
 
6.2%
R 977794
 
6.2%
C 869762
 
5.5%
D 489377
 
3.1%
( 488897
 
3.1%
Other values (5) 1467171
9.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 15759922
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
E 2717798
17.2%
2336453
14.8%
N 2228661
14.1%
O 1847556
11.7%
S 1358659
8.6%
T 977794
 
6.2%
R 977794
 
6.2%
C 869762
 
5.5%
D 489377
 
3.1%
( 488897
 
3.1%
Other values (5) 1467171
9.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 15759922
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
E 2717798
17.2%
2336453
14.8%
N 2228661
14.1%
O 1847556
11.7%
S 1358659
8.6%
T 977794
 
6.2%
R 977794
 
6.2%
C 869762
 
5.5%
D 489377
 
3.1%
( 488897
 
3.1%
Other values (5) 1467171
9.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 15759922
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
E 2717798
17.2%
2336453
14.8%
N 2228661
14.1%
O 1847556
11.7%
S 1358659
8.6%
T 977794
 
6.2%
R 977794
 
6.2%
C 869762
 
5.5%
D 489377
 
3.1%
( 488897
 
3.1%
Other values (5) 1467171
9.3%

CRASH_TYPE
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.9 MiB
NO INJURY / DRIVE AWAY
656245 
INJURY AND / OR TOW DUE TO CRASH
241635 

Length

Max length32
Median length22
Mean length24.691173
Min length22

Characters and Unicode

Total characters22169710
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowINJURY AND / OR TOW DUE TO CRASH
2nd rowNO INJURY / DRIVE AWAY
3rd rowNO INJURY / DRIVE AWAY
4th rowNO INJURY / DRIVE AWAY
5th rowINJURY AND / OR TOW DUE TO CRASH

Common Values

ValueCountFrequency (%)
NO INJURY / DRIVE AWAY 656245
73.1%
INJURY AND / OR TOW DUE TO CRASH 241635
 
26.9%

Length

2024-12-04T12:58:06.090610image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-04T12:58:06.194610image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
ValueCountFrequency (%)
injury 897880
17.2%
897880
17.2%
no 656245
12.6%
drive 656245
12.6%
away 656245
12.6%
and 241635
 
4.6%
or 241635
 
4.6%
tow 241635
 
4.6%
due 241635
 
4.6%
to 241635
 
4.6%

Most occurring characters

ValueCountFrequency (%)
4316425
19.5%
R 2037395
9.2%
A 1795760
 
8.1%
N 1795760
 
8.1%
I 1554125
 
7.0%
Y 1554125
 
7.0%
O 1381150
 
6.2%
D 1139515
 
5.1%
U 1139515
 
5.1%
J 897880
 
4.1%
Other values (8) 4558060
20.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 22169710
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
4316425
19.5%
R 2037395
9.2%
A 1795760
 
8.1%
N 1795760
 
8.1%
I 1554125
 
7.0%
Y 1554125
 
7.0%
O 1381150
 
6.2%
D 1139515
 
5.1%
U 1139515
 
5.1%
J 897880
 
4.1%
Other values (8) 4558060
20.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 22169710
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
4316425
19.5%
R 2037395
9.2%
A 1795760
 
8.1%
N 1795760
 
8.1%
I 1554125
 
7.0%
Y 1554125
 
7.0%
O 1381150
 
6.2%
D 1139515
 
5.1%
U 1139515
 
5.1%
J 897880
 
4.1%
Other values (8) 4558060
20.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 22169710
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
4316425
19.5%
R 2037395
9.2%
A 1795760
 
8.1%
N 1795760
 
8.1%
I 1554125
 
7.0%
Y 1554125
 
7.0%
O 1381150
 
6.2%
D 1139515
 
5.1%
U 1139515
 
5.1%
J 897880
 
4.1%
Other values (8) 4558060
20.6%

INTERSECTION_RELATED_I
Boolean

High correlation  Imbalance  Missing 

Distinct2
Distinct (%)< 0.1%
Missing691701
Missing (%)77.0%
Memory size1.7 MiB
True
196346 
False
 
9833
(Missing)
691701 
ValueCountFrequency (%)
True 196346
 
21.9%
False 9833
 
1.1%
(Missing) 691701
77.0%
2024-12-04T12:58:06.296610image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

NOT_RIGHT_OF_WAY_I
Boolean

High correlation  Imbalance  Missing 

Distinct2
Distinct (%)< 0.1%
Missing856988
Missing (%)95.4%
Memory size1.7 MiB
True
 
37138
False
 
3754
(Missing)
856988 
ValueCountFrequency (%)
True 37138
 
4.1%
False 3754
 
0.4%
(Missing) 856988
95.4%
2024-12-04T12:58:06.496610image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

HIT_AND_RUN_I
Boolean

High correlation  Imbalance  Missing 

Distinct2
Distinct (%)< 0.1%
Missing616240
Missing (%)68.6%
Memory size1.7 MiB
True
269575 
False
 
12065
(Missing)
616240 
ValueCountFrequency (%)
True 269575
30.0%
False 12065
 
1.3%
(Missing) 616240
68.6%
2024-12-04T12:58:06.584610image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

DAMAGE
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.9 MiB
OVER $1,500
565060 
$501 - $1,500
232007 
$500 OR LESS
100813 

Length

Max length13
Median length11
Mean length11.629067
Min length11

Characters and Unicode

Total characters10441507
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowOVER $1,500
2nd rowOVER $1,500
3rd rowOVER $1,500
4th rowOVER $1,500
5th rowOVER $1,500

Common Values

ValueCountFrequency (%)
OVER $1,500 565060
62.9%
$501 - $1,500 232007
25.8%
$500 OR LESS 100813
 
11.2%

Length

2024-12-04T12:58:06.695611image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-04T12:58:06.938979image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
ValueCountFrequency (%)
1,500 797067
37.4%
over 565060
26.5%
501 232007
 
10.9%
232007
 
10.9%
500 100813
 
4.7%
or 100813
 
4.7%
less 100813
 
4.7%

Most occurring characters

ValueCountFrequency (%)
0 2027767
19.4%
1230700
11.8%
$ 1129887
10.8%
5 1129887
10.8%
1 1029074
9.9%
, 797067
 
7.6%
R 665873
 
6.4%
E 665873
 
6.4%
O 665873
 
6.4%
V 565060
 
5.4%
Other values (3) 534446
 
5.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 10441507
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 2027767
19.4%
1230700
11.8%
$ 1129887
10.8%
5 1129887
10.8%
1 1029074
9.9%
, 797067
 
7.6%
R 665873
 
6.4%
E 665873
 
6.4%
O 665873
 
6.4%
V 565060
 
5.4%
Other values (3) 534446
 
5.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 10441507
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 2027767
19.4%
1230700
11.8%
$ 1129887
10.8%
5 1129887
10.8%
1 1029074
9.9%
, 797067
 
7.6%
R 665873
 
6.4%
E 665873
 
6.4%
O 665873
 
6.4%
V 565060
 
5.4%
Other values (3) 534446
 
5.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 10441507
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 2027767
19.4%
1230700
11.8%
$ 1129887
10.8%
5 1129887
10.8%
1 1029074
9.9%
, 797067
 
7.6%
R 665873
 
6.4%
E 665873
 
6.4%
O 665873
 
6.4%
V 565060
 
5.4%
Other values (3) 534446
 
5.1%
Distinct680121
Distinct (%)75.7%
Missing0
Missing (%)0.0%
Memory size6.9 MiB
Minimum2013-06-01 20:31:00
Maximum2024-12-02 02:45:00
2024-12-04T12:58:07.070979image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:58:07.220979image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct40
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.9 MiB
UNABLE TO DETERMINE
351230 
FAILING TO YIELD RIGHT-OF-WAY
99122 
FOLLOWING TOO CLOSELY
86667 
NOT APPLICABLE
47458 
IMPROPER OVERTAKING/PASSING
44741 
Other values (35)
268662 

Length

Max length80
Median length75
Mean length23.718016
Min length6

Characters and Unicode

Total characters21295932
Distinct characters33
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowUNABLE TO DETERMINE
2nd rowFOLLOWING TOO CLOSELY
3rd rowFAILING TO REDUCE SPEED TO AVOID CRASH
4th rowFAILING TO YIELD RIGHT-OF-WAY
5th rowFOLLOWING TOO CLOSELY

Common Values

ValueCountFrequency (%)
UNABLE TO DETERMINE 351230
39.1%
FAILING TO YIELD RIGHT-OF-WAY 99122
 
11.0%
FOLLOWING TOO CLOSELY 86667
 
9.7%
NOT APPLICABLE 47458
 
5.3%
IMPROPER OVERTAKING/PASSING 44741
 
5.0%
FAILING TO REDUCE SPEED TO AVOID CRASH 37724
 
4.2%
IMPROPER BACKING 34686
 
3.9%
IMPROPER LANE USAGE 31987
 
3.6%
DRIVING SKILLS/KNOWLEDGE/EXPERIENCE 30469
 
3.4%
IMPROPER TURNING/NO SIGNAL 30073
 
3.3%
Other values (30) 103723
 
11.6%

Length

2024-12-04T12:58:07.385979image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
to 527897
17.9%
unable 351230
 
11.9%
determine 351230
 
11.9%
improper 141487
 
4.8%
failing 136846
 
4.6%
yield 99401
 
3.4%
right-of-way 99122
 
3.4%
following 86667
 
2.9%
too 86667
 
2.9%
closely 86667
 
2.9%
Other values (106) 985019
33.4%

Most occurring characters

ValueCountFrequency (%)
E 2613852
12.3%
2054353
 
9.6%
I 1802613
 
8.5%
N 1609243
 
7.6%
O 1544471
 
7.3%
T 1359514
 
6.4%
L 1297386
 
6.1%
R 1229838
 
5.8%
A 1173420
 
5.5%
G 791429
 
3.7%
Other values (23) 5819813
27.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 21295932
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
E 2613852
12.3%
2054353
 
9.6%
I 1802613
 
8.5%
N 1609243
 
7.6%
O 1544471
 
7.3%
T 1359514
 
6.4%
L 1297386
 
6.1%
R 1229838
 
5.8%
A 1173420
 
5.5%
G 791429
 
3.7%
Other values (23) 5819813
27.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 21295932
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
E 2613852
12.3%
2054353
 
9.6%
I 1802613
 
8.5%
N 1609243
 
7.6%
O 1544471
 
7.3%
T 1359514
 
6.4%
L 1297386
 
6.1%
R 1229838
 
5.8%
A 1173420
 
5.5%
G 791429
 
3.7%
Other values (23) 5819813
27.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 21295932
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
E 2613852
12.3%
2054353
 
9.6%
I 1802613
 
8.5%
N 1609243
 
7.6%
O 1544471
 
7.3%
T 1359514
 
6.4%
L 1297386
 
6.1%
R 1229838
 
5.8%
A 1173420
 
5.5%
G 791429
 
3.7%
Other values (23) 5819813
27.3%

SEC_CONTRIBUTORY_CAUSE
Categorical

Imbalance 

Distinct40
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.9 MiB
NOT APPLICABLE
370111 
UNABLE TO DETERMINE
323670 
FAILING TO REDUCE SPEED TO AVOID CRASH
 
33044
FAILING TO YIELD RIGHT-OF-WAY
 
28782
DRIVING SKILLS/KNOWLEDGE/EXPERIENCE
 
27972
Other values (35)
114301 

Length

Max length80
Median length75
Mean length19.462895
Min length6

Characters and Unicode

Total characters17475344
Distinct characters33
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNOT APPLICABLE
2nd rowFOLLOWING TOO CLOSELY
3rd rowOPERATING VEHICLE IN ERRATIC, RECKLESS, CARELESS, NEGLIGENT OR AGGRESSIVE MANNER
4th rowNOT APPLICABLE
5th rowDISTRACTION - FROM INSIDE VEHICLE

Common Values

ValueCountFrequency (%)
NOT APPLICABLE 370111
41.2%
UNABLE TO DETERMINE 323670
36.0%
FAILING TO REDUCE SPEED TO AVOID CRASH 33044
 
3.7%
FAILING TO YIELD RIGHT-OF-WAY 28782
 
3.2%
DRIVING SKILLS/KNOWLEDGE/EXPERIENCE 27972
 
3.1%
FOLLOWING TOO CLOSELY 23639
 
2.6%
IMPROPER OVERTAKING/PASSING 13956
 
1.6%
IMPROPER LANE USAGE 12636
 
1.4%
WEATHER 9906
 
1.1%
IMPROPER TURNING/NO SIGNAL 9329
 
1.0%
Other values (30) 44835
 
5.0%

Length

2024-12-04T12:58:07.535980image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
to 419441
16.7%
not 371170
14.8%
applicable 370111
14.8%
unable 323670
12.9%
determine 323670
12.9%
failing 61826
 
2.5%
improper 43085
 
1.7%
speed 35955
 
1.4%
avoid 33044
 
1.3%
crash 33044
 
1.3%
Other values (106) 493982
19.7%

Most occurring characters

ValueCountFrequency (%)
E 2291564
13.1%
1611118
 
9.2%
L 1405855
 
8.0%
N 1388119
 
7.9%
A 1375335
 
7.9%
I 1290297
 
7.4%
T 1268560
 
7.3%
O 1133644
 
6.5%
P 918590
 
5.3%
B 711670
 
4.1%
Other values (23) 4080592
23.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 17475344
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
E 2291564
13.1%
1611118
 
9.2%
L 1405855
 
8.0%
N 1388119
 
7.9%
A 1375335
 
7.9%
I 1290297
 
7.4%
T 1268560
 
7.3%
O 1133644
 
6.5%
P 918590
 
5.3%
B 711670
 
4.1%
Other values (23) 4080592
23.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 17475344
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
E 2291564
13.1%
1611118
 
9.2%
L 1405855
 
8.0%
N 1388119
 
7.9%
A 1375335
 
7.9%
I 1290297
 
7.4%
T 1268560
 
7.3%
O 1133644
 
6.5%
P 918590
 
5.3%
B 711670
 
4.1%
Other values (23) 4080592
23.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 17475344
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
E 2291564
13.1%
1611118
 
9.2%
L 1405855
 
8.0%
N 1388119
 
7.9%
A 1375335
 
7.9%
I 1290297
 
7.4%
T 1268560
 
7.3%
O 1133644
 
6.5%
P 918590
 
5.3%
B 711670
 
4.1%
Other values (23) 4080592
23.4%

STREET_NO
Real number (ℝ)

High correlation 

Distinct11827
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3687.9654
Minimum0
Maximum451100
Zeros5
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size6.9 MiB
2024-12-04T12:58:07.689980image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile150
Q11253
median3201
Q35559
95-th percentile9036
Maximum451100
Range451100
Interquartile range (IQR)4306

Descriptive statistics

Standard deviation2879.5297
Coefficient of variation (CV)0.78079086
Kurtosis649.00537
Mean3687.9654
Median Absolute Deviation (MAD)2100
Skewness4.9051114
Sum3.3113504 × 109
Variance8291691
MonotonicityNot monotonic
2024-12-04T12:58:07.853003image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1600 5722
 
0.6%
100 5275
 
0.6%
800 5186
 
0.6%
200 4931
 
0.5%
300 4402
 
0.5%
2400 4360
 
0.5%
4700 4349
 
0.5%
1200 4261
 
0.5%
500 4217
 
0.5%
7900 4099
 
0.5%
Other values (11817) 851078
94.8%
ValueCountFrequency (%)
0 5
 
< 0.1%
1 3746
0.4%
2 1838
0.2%
3 767
 
0.1%
4 155
 
< 0.1%
5 695
 
0.1%
6 227
 
< 0.1%
7 178
 
< 0.1%
8 214
 
< 0.1%
9 188
 
< 0.1%
ValueCountFrequency (%)
451100 1
 
< 0.1%
34453 1
 
< 0.1%
13799 6
 
< 0.1%
13795 1
 
< 0.1%
13787 1
 
< 0.1%
13781 1
 
< 0.1%
13780 1
 
< 0.1%
13770 30
< 0.1%
13768 1
 
< 0.1%
13763 1
 
< 0.1%

STREET_DIRECTION
Categorical

Distinct4
Distinct (%)< 0.1%
Missing4
Missing (%)< 0.1%
Memory size6.9 MiB
W
321516 
S
299873 
N
215870 
E
60617 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters897876
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowS
2nd rowS
3rd rowS
4th rowW
5th rowW

Common Values

ValueCountFrequency (%)
W 321516
35.8%
S 299873
33.4%
N 215870
24.0%
E 60617
 
6.8%
(Missing) 4
 
< 0.1%

Length

2024-12-04T12:58:07.970000image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-04T12:58:08.081001image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
ValueCountFrequency (%)
w 321516
35.8%
s 299873
33.4%
n 215870
24.0%
e 60617
 
6.8%

Most occurring characters

ValueCountFrequency (%)
W 321516
35.8%
S 299873
33.4%
N 215870
24.0%
E 60617
 
6.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 897876
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
W 321516
35.8%
S 299873
33.4%
N 215870
24.0%
E 60617
 
6.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 897876
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
W 321516
35.8%
S 299873
33.4%
N 215870
24.0%
E 60617
 
6.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 897876
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
W 321516
35.8%
S 299873
33.4%
N 215870
24.0%
E 60617
 
6.8%
Distinct1648
Distinct (%)0.2%
Missing1
Missing (%)< 0.1%
Memory size6.9 MiB
2024-12-04T12:58:08.345008image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Length

Max length31
Median length24
Mean length10.680425
Min length4

Characters and Unicode

Total characters9589729
Distinct characters40
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique137 ?
Unique (%)< 0.1%

Sample

1st rowWENTWORTH AVE
2nd rowCHICAGO SKYWAY OB
3rd rowASHLAND AVE
4th rowBALMORAL AVE
5th rowOHARE ST
ValueCountFrequency (%)
ave 455268
23.7%
st 278374
 
14.5%
rd 58325
 
3.0%
dr 55173
 
2.9%
blvd 34127
 
1.8%
lake 28593
 
1.5%
western 26129
 
1.4%
shore 24022
 
1.3%
pulaski 21700
 
1.1%
cicero 20190
 
1.1%
Other values (1351) 917778
47.8%
2024-12-04T12:58:08.769005image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
E 1099102
11.5%
A 1047193
10.9%
1021801
 
10.7%
T 679827
 
7.1%
S 632010
 
6.6%
R 614969
 
6.4%
V 555803
 
5.8%
N 481421
 
5.0%
L 447948
 
4.7%
O 401021
 
4.2%
Other values (30) 2608634
27.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 9589729
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
E 1099102
11.5%
A 1047193
10.9%
1021801
 
10.7%
T 679827
 
7.1%
S 632010
 
6.6%
R 614969
 
6.4%
V 555803
 
5.8%
N 481421
 
5.0%
L 447948
 
4.7%
O 401021
 
4.2%
Other values (30) 2608634
27.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 9589729
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
E 1099102
11.5%
A 1047193
10.9%
1021801
 
10.7%
T 679827
 
7.1%
S 632010
 
6.6%
R 614969
 
6.4%
V 555803
 
5.8%
N 481421
 
5.0%
L 447948
 
4.7%
O 401021
 
4.2%
Other values (30) 2608634
27.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 9589729
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
E 1099102
11.5%
A 1047193
10.9%
1021801
 
10.7%
T 679827
 
7.1%
S 632010
 
6.6%
R 614969
 
6.4%
V 555803
 
5.8%
N 481421
 
5.0%
L 447948
 
4.7%
O 401021
 
4.2%
Other values (30) 2608634
27.2%

BEAT_OF_OCCURRENCE
Real number (ℝ)

High correlation 

Distinct276
Distinct (%)< 0.1%
Missing5
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean1245.4846
Minimum111
Maximum6100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.9 MiB
2024-12-04T12:58:08.910003image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Quantile statistics

Minimum111
5-th percentile131
Q1715
median1212
Q31822
95-th percentile2513
Maximum6100
Range5989
Interquartile range (IQR)1107

Descriptive statistics

Standard deviation704.84186
Coefficient of variation (CV)0.56591778
Kurtosis-0.98963934
Mean1245.4846
Median Absolute Deviation (MAD)580
Skewness0.17636727
Sum1.1182894 × 109
Variance496802.04
MonotonicityNot monotonic
2024-12-04T12:58:09.048003image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1834 10868
 
1.2%
114 9250
 
1.0%
813 9049
 
1.0%
815 8558
 
1.0%
1831 8211
 
0.9%
122 7708
 
0.9%
833 7332
 
0.8%
834 6786
 
0.8%
2413 6735
 
0.8%
2512 6567
 
0.7%
Other values (266) 816811
91.0%
ValueCountFrequency (%)
111 3882
0.4%
112 2745
 
0.3%
113 2103
 
0.2%
114 9250
1.0%
121 4158
0.5%
122 7708
0.9%
123 5506
0.6%
124 4689
0.5%
131 5289
0.6%
132 5579
0.6%
ValueCountFrequency (%)
6100 7
 
< 0.1%
2535 2861
0.3%
2534 3847
0.4%
2533 6381
0.7%
2532 2513
 
0.3%
2531 2417
 
0.3%
2525 1976
 
0.2%
2524 2842
0.3%
2523 3143
0.4%
2522 3728
0.4%

PHOTOS_TAKEN_I
Boolean

High correlation  Missing 

Distinct2
Distinct (%)< 0.1%
Missing885622
Missing (%)98.6%
Memory size1.7 MiB
True
 
9229
False
 
3029
(Missing)
885622 
ValueCountFrequency (%)
True 9229
 
1.0%
False 3029
 
0.3%
(Missing) 885622
98.6%
2024-12-04T12:58:09.173003image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

STATEMENTS_TAKEN_I
Boolean

High correlation  Missing 

Distinct2
Distinct (%)< 0.1%
Missing877235
Missing (%)97.7%
Memory size1.7 MiB
True
 
16905
False
 
3740
(Missing)
877235 
ValueCountFrequency (%)
True 16905
 
1.9%
False 3740
 
0.4%
(Missing) 877235
97.7%
2024-12-04T12:58:09.271036image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

DOORING_I
Boolean

High correlation  Missing 

Distinct2
Distinct (%)0.1%
Missing895036
Missing (%)99.7%
Memory size1.7 MiB
True
 
1913
False
 
931
(Missing)
895036 
ValueCountFrequency (%)
True 1913
 
0.2%
False 931
 
0.1%
(Missing) 895036
99.7%
2024-12-04T12:58:09.393034image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

WORK_ZONE_I
Boolean

High correlation  Missing 

Distinct2
Distinct (%)< 0.1%
Missing892860
Missing (%)99.4%
Memory size1.7 MiB
True
 
3879
False
 
1141
(Missing)
892860 
ValueCountFrequency (%)
True 3879
 
0.4%
False 1141
 
0.1%
(Missing) 892860
99.4%
2024-12-04T12:58:09.504037image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

WORK_ZONE_TYPE
Categorical

High correlation  Missing 

Distinct4
Distinct (%)0.1%
Missing894001
Missing (%)99.6%
Memory size6.9 MiB
CONSTRUCTION
2693 
UNKNOWN
544 
MAINTENANCE
400 
UTILITY
 
242

Length

Max length12
Median length12
Mean length10.883733
Min length7

Characters and Unicode

Total characters42218
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCONSTRUCTION
2nd rowCONSTRUCTION
3rd rowCONSTRUCTION
4th rowCONSTRUCTION
5th rowCONSTRUCTION

Common Values

ValueCountFrequency (%)
CONSTRUCTION 2693
 
0.3%
UNKNOWN 544
 
0.1%
MAINTENANCE 400
 
< 0.1%
UTILITY 242
 
< 0.1%
(Missing) 894001
99.6%

Length

2024-12-04T12:58:09.665049image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-04T12:58:09.825036image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
ValueCountFrequency (%)
construction 2693
69.4%
unknown 544
 
14.0%
maintenance 400
 
10.3%
utility 242
 
6.2%

Most occurring characters

ValueCountFrequency (%)
N 8218
19.5%
T 6270
14.9%
O 5930
14.0%
C 5786
13.7%
I 3577
8.5%
U 3479
8.2%
S 2693
 
6.4%
R 2693
 
6.4%
A 800
 
1.9%
E 800
 
1.9%
Other values (5) 1972
 
4.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 42218
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 8218
19.5%
T 6270
14.9%
O 5930
14.0%
C 5786
13.7%
I 3577
8.5%
U 3479
8.2%
S 2693
 
6.4%
R 2693
 
6.4%
A 800
 
1.9%
E 800
 
1.9%
Other values (5) 1972
 
4.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 42218
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 8218
19.5%
T 6270
14.9%
O 5930
14.0%
C 5786
13.7%
I 3577
8.5%
U 3479
8.2%
S 2693
 
6.4%
R 2693
 
6.4%
A 800
 
1.9%
E 800
 
1.9%
Other values (5) 1972
 
4.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 42218
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 8218
19.5%
T 6270
14.9%
O 5930
14.0%
C 5786
13.7%
I 3577
8.5%
U 3479
8.2%
S 2693
 
6.4%
R 2693
 
6.4%
A 800
 
1.9%
E 800
 
1.9%
Other values (5) 1972
 
4.7%

WORKERS_PRESENT_I
Boolean

High correlation  Missing 

Distinct2
Distinct (%)0.2%
Missing896584
Missing (%)99.9%
Memory size1.7 MiB
True
 
1149
False
 
147
(Missing)
896584 
ValueCountFrequency (%)
True 1149
 
0.1%
False 147
 
< 0.1%
(Missing) 896584
99.9%
2024-12-04T12:58:09.980034image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

NUM_UNITS
Real number (ℝ)

Distinct17
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.0352319
Minimum1
Maximum18
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.9 MiB
2024-12-04T12:58:10.081036image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median2
Q32
95-th percentile3
Maximum18
Range17
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.45195466
Coefficient of variation (CV)0.22206544
Kurtosis40.000274
Mean2.0352319
Median Absolute Deviation (MAD)0
Skewness3.3876972
Sum1827394
Variance0.20426302
MonotonicityNot monotonic
2024-12-04T12:58:10.214037image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
2 786228
87.6%
3 49528
 
5.5%
1 49147
 
5.5%
4 9564
 
1.1%
5 2334
 
0.3%
6 669
 
0.1%
7 227
 
< 0.1%
8 99
 
< 0.1%
9 40
 
< 0.1%
10 20
 
< 0.1%
Other values (7) 24
 
< 0.1%
ValueCountFrequency (%)
1 49147
 
5.5%
2 786228
87.6%
3 49528
 
5.5%
4 9564
 
1.1%
5 2334
 
0.3%
6 669
 
0.1%
7 227
 
< 0.1%
8 99
 
< 0.1%
9 40
 
< 0.1%
10 20
 
< 0.1%
ValueCountFrequency (%)
18 5
 
< 0.1%
16 2
 
< 0.1%
15 1
 
< 0.1%
14 2
 
< 0.1%
13 1
 
< 0.1%
12 5
 
< 0.1%
11 8
 
< 0.1%
10 20
 
< 0.1%
9 40
< 0.1%
8 99
< 0.1%

MOST_SEVERE_INJURY
Categorical

High correlation  Imbalance 

Distinct5
Distinct (%)< 0.1%
Missing1988
Missing (%)0.2%
Memory size6.9 MiB
NO INDICATION OF INJURY
769758 
NONINCAPACITATING INJURY
 
70855
REPORTED, NOT EVIDENT
 
39259
INCAPACITATING INJURY
 
15038
FATAL
 
982

Length

Max length24
Median length23
Mean length22.938145
Min length5

Characters and Unicode

Total characters20550101
Distinct characters19
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowINCAPACITATING INJURY
2nd rowNO INDICATION OF INJURY
3rd rowNO INDICATION OF INJURY
4th rowNO INDICATION OF INJURY
5th rowNONINCAPACITATING INJURY

Common Values

ValueCountFrequency (%)
NO INDICATION OF INJURY 769758
85.7%
NONINCAPACITATING INJURY 70855
 
7.9%
REPORTED, NOT EVIDENT 39259
 
4.4%
INCAPACITATING INJURY 15038
 
1.7%
FATAL 982
 
0.1%
(Missing) 1988
 
0.2%

Length

2024-12-04T12:58:10.504036image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-04T12:58:10.626036image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
ValueCountFrequency (%)
injury 855651
25.4%
no 769758
22.8%
indication 769758
22.8%
of 769758
22.8%
nonincapacitating 70855
 
2.1%
reported 39259
 
1.2%
not 39259
 
1.2%
evident 39259
 
1.2%
incapacitating 15038
 
0.4%
fatal 982
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
N 3556939
17.3%
I 3461863
16.8%
2473685
12.0%
O 2458647
12.0%
T 1060303
 
5.2%
A 1029401
 
5.0%
C 941544
 
4.6%
R 934169
 
4.5%
U 855651
 
4.2%
Y 855651
 
4.2%
Other values (9) 2922248
14.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 20550101
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 3556939
17.3%
I 3461863
16.8%
2473685
12.0%
O 2458647
12.0%
T 1060303
 
5.2%
A 1029401
 
5.0%
C 941544
 
4.6%
R 934169
 
4.5%
U 855651
 
4.2%
Y 855651
 
4.2%
Other values (9) 2922248
14.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 20550101
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 3556939
17.3%
I 3461863
16.8%
2473685
12.0%
O 2458647
12.0%
T 1060303
 
5.2%
A 1029401
 
5.0%
C 941544
 
4.6%
R 934169
 
4.5%
U 855651
 
4.2%
Y 855651
 
4.2%
Other values (9) 2922248
14.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 20550101
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 3556939
17.3%
I 3461863
16.8%
2473685
12.0%
O 2458647
12.0%
T 1060303
 
5.2%
A 1029401
 
5.0%
C 941544
 
4.6%
R 934169
 
4.5%
U 855651
 
4.2%
Y 855651
 
4.2%
Other values (9) 2922248
14.2%

INJURIES_TOTAL
Real number (ℝ)

High correlation  Zeros 

Distinct20
Distinct (%)< 0.1%
Missing1974
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean0.19419336
Minimum0
Maximum21
Zeros769772
Zeros (%)85.7%
Negative0
Negative (%)0.0%
Memory size6.9 MiB
2024-12-04T12:58:10.742036image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum21
Range21
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.5723508
Coefficient of variation (CV)2.9473242
Kurtosis43.808227
Mean0.19419336
Median Absolute Deviation (MAD)0
Skewness4.7624072
Sum173979
Variance0.32758544
MonotonicityNot monotonic
2024-12-04T12:58:10.916100image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
0 769772
85.7%
1 94798
 
10.6%
2 21181
 
2.4%
3 6451
 
0.7%
4 2297
 
0.3%
5 822
 
0.1%
6 324
 
< 0.1%
7 131
 
< 0.1%
8 53
 
< 0.1%
9 27
 
< 0.1%
Other values (10) 50
 
< 0.1%
(Missing) 1974
 
0.2%
ValueCountFrequency (%)
0 769772
85.7%
1 94798
 
10.6%
2 21181
 
2.4%
3 6451
 
0.7%
4 2297
 
0.3%
5 822
 
0.1%
6 324
 
< 0.1%
7 131
 
< 0.1%
8 53
 
< 0.1%
9 27
 
< 0.1%
ValueCountFrequency (%)
21 4
 
< 0.1%
19 1
 
< 0.1%
17 1
 
< 0.1%
16 1
 
< 0.1%
15 8
< 0.1%
14 1
 
< 0.1%
13 3
 
< 0.1%
12 6
 
< 0.1%
11 9
< 0.1%
10 16
< 0.1%

INJURIES_FATAL
Categorical

Imbalance 

Distinct5
Distinct (%)< 0.1%
Missing1974
Missing (%)0.2%
Memory size6.9 MiB
0.0
894924 
1.0
 
909
2.0
 
64
3.0
 
8
4.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters2687718
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 894924
99.7%
1.0 909
 
0.1%
2.0 64
 
< 0.1%
3.0 8
 
< 0.1%
4.0 1
 
< 0.1%
(Missing) 1974
 
0.2%

Length

2024-12-04T12:58:11.055098image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-04T12:58:11.201096image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 894924
99.9%
1.0 909
 
0.1%
2.0 64
 
< 0.1%
3.0 8
 
< 0.1%
4.0 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 1790830
66.6%
. 895906
33.3%
1 909
 
< 0.1%
2 64
 
< 0.1%
3 8
 
< 0.1%
4 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2687718
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1790830
66.6%
. 895906
33.3%
1 909
 
< 0.1%
2 64
 
< 0.1%
3 8
 
< 0.1%
4 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2687718
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1790830
66.6%
. 895906
33.3%
1 909
 
< 0.1%
2 64
 
< 0.1%
3 8
 
< 0.1%
4 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2687718
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1790830
66.6%
. 895906
33.3%
1 909
 
< 0.1%
2 64
 
< 0.1%
3 8
 
< 0.1%
4 1
 
< 0.1%

INJURIES_INCAPACITATING
Real number (ℝ)

Zeros 

Distinct10
Distinct (%)< 0.1%
Missing1974
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean0.019754305
Minimum0
Maximum10
Zeros880718
Zeros (%)98.1%
Negative0
Negative (%)0.0%
Memory size6.9 MiB
2024-12-04T12:58:11.320096image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum10
Range10
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.16464574
Coefficient of variation (CV)8.3346764
Kurtosis194.93791
Mean0.019754305
Median Absolute Deviation (MAD)0
Skewness11.38532
Sum17698
Variance0.027108218
MonotonicityNot monotonic
2024-12-04T12:58:11.457096image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
0 880718
98.1%
1 13339
 
1.5%
2 1393
 
0.2%
3 310
 
< 0.1%
4 106
 
< 0.1%
5 29
 
< 0.1%
6 7
 
< 0.1%
7 2
 
< 0.1%
10 1
 
< 0.1%
8 1
 
< 0.1%
(Missing) 1974
 
0.2%
ValueCountFrequency (%)
0 880718
98.1%
1 13339
 
1.5%
2 1393
 
0.2%
3 310
 
< 0.1%
4 106
 
< 0.1%
5 29
 
< 0.1%
6 7
 
< 0.1%
7 2
 
< 0.1%
8 1
 
< 0.1%
10 1
 
< 0.1%
ValueCountFrequency (%)
10 1
 
< 0.1%
8 1
 
< 0.1%
7 2
 
< 0.1%
6 7
 
< 0.1%
5 29
 
< 0.1%
4 106
 
< 0.1%
3 310
 
< 0.1%
2 1393
 
0.2%
1 13339
 
1.5%
0 880718
98.1%

INJURIES_NON_INCAPACITATING
Real number (ℝ)

High correlation  Zeros 

Distinct20
Distinct (%)< 0.1%
Missing1974
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean0.1087949
Minimum0
Maximum21
Zeros822306
Zeros (%)91.6%
Negative0
Negative (%)0.0%
Memory size6.9 MiB
2024-12-04T12:58:11.609607image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum21
Range21
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.42531625
Coefficient of variation (CV)3.9093401
Kurtosis77.511065
Mean0.1087949
Median Absolute Deviation (MAD)0
Skewness6.2951477
Sum97470
Variance0.18089391
MonotonicityNot monotonic
2024-12-04T12:58:11.719595image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
0 822306
91.6%
1 57677
 
6.4%
2 10962
 
1.2%
3 3198
 
0.4%
4 1109
 
0.1%
5 385
 
< 0.1%
6 159
 
< 0.1%
7 53
 
< 0.1%
8 22
 
< 0.1%
10 9
 
< 0.1%
Other values (10) 26
 
< 0.1%
(Missing) 1974
 
0.2%
ValueCountFrequency (%)
0 822306
91.6%
1 57677
 
6.4%
2 10962
 
1.2%
3 3198
 
0.4%
4 1109
 
0.1%
5 385
 
< 0.1%
6 159
 
< 0.1%
7 53
 
< 0.1%
8 22
 
< 0.1%
9 8
 
< 0.1%
ValueCountFrequency (%)
21 2
 
< 0.1%
19 1
 
< 0.1%
18 1
 
< 0.1%
16 1
 
< 0.1%
15 1
 
< 0.1%
14 1
 
< 0.1%
13 1
 
< 0.1%
12 4
< 0.1%
11 6
< 0.1%
10 9
< 0.1%

INJURIES_REPORTED_NOT_EVIDENT
Real number (ℝ)

High correlation  Zeros 

Distinct13
Distinct (%)< 0.1%
Missing1974
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean0.064455423
Minimum0
Maximum15
Zeros852119
Zeros (%)94.9%
Negative0
Negative (%)0.0%
Memory size6.9 MiB
2024-12-04T12:58:11.810595image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum15
Range15
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.32641532
Coefficient of variation (CV)5.0642027
Kurtosis90.467454
Mean0.064455423
Median Absolute Deviation (MAD)0
Skewness7.4971597
Sum57746
Variance0.10654696
MonotonicityNot monotonic
2024-12-04T12:58:11.965627image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
0 852119
94.9%
1 34008
 
3.8%
2 7056
 
0.8%
3 1830
 
0.2%
4 579
 
0.1%
5 199
 
< 0.1%
6 55
 
< 0.1%
7 27
 
< 0.1%
8 13
 
< 0.1%
9 10
 
< 0.1%
Other values (3) 10
 
< 0.1%
(Missing) 1974
 
0.2%
ValueCountFrequency (%)
0 852119
94.9%
1 34008
 
3.8%
2 7056
 
0.8%
3 1830
 
0.2%
4 579
 
0.1%
5 199
 
< 0.1%
6 55
 
< 0.1%
7 27
 
< 0.1%
8 13
 
< 0.1%
9 10
 
< 0.1%
ValueCountFrequency (%)
15 2
 
< 0.1%
11 2
 
< 0.1%
10 6
 
< 0.1%
9 10
 
< 0.1%
8 13
 
< 0.1%
7 27
 
< 0.1%
6 55
 
< 0.1%
5 199
 
< 0.1%
4 579
 
0.1%
3 1830
0.2%

INJURIES_NO_INDICATION
Real number (ℝ)

Zeros 

Distinct50
Distinct (%)< 0.1%
Missing1974
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean2.0010481
Minimum0
Maximum61
Zeros19237
Zeros (%)2.1%
Negative0
Negative (%)0.0%
Memory size6.9 MiB
2024-12-04T12:58:12.095595image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median2
Q32
95-th percentile4
Maximum61
Range61
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.157458
Coefficient of variation (CV)0.57842588
Kurtosis71.965418
Mean2.0010481
Median Absolute Deviation (MAD)1
Skewness3.818708
Sum1792751
Variance1.339709
MonotonicityNot monotonic
2024-12-04T12:58:12.234595image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 416578
46.4%
1 274652
30.6%
3 112877
 
12.6%
4 42155
 
4.7%
0 19237
 
2.1%
5 17687
 
2.0%
6 7346
 
0.8%
7 2855
 
0.3%
8 1273
 
0.1%
9 527
 
0.1%
Other values (40) 719
 
0.1%
(Missing) 1974
 
0.2%
ValueCountFrequency (%)
0 19237
 
2.1%
1 274652
30.6%
2 416578
46.4%
3 112877
 
12.6%
4 42155
 
4.7%
5 17687
 
2.0%
6 7346
 
0.8%
7 2855
 
0.3%
8 1273
 
0.1%
9 527
 
0.1%
ValueCountFrequency (%)
61 1
 
< 0.1%
50 1
 
< 0.1%
49 1
 
< 0.1%
48 1
 
< 0.1%
46 1
 
< 0.1%
45 2
< 0.1%
43 1
 
< 0.1%
42 4
< 0.1%
41 1
 
< 0.1%
40 2
< 0.1%

INJURIES_UNKNOWN
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing1974
Missing (%)0.2%
Memory size6.9 MiB
0.0
895906 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters2687718
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 895906
99.8%
(Missing) 1974
 
0.2%

Length

2024-12-04T12:58:12.397606image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-04T12:58:12.517606image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 895906
100.0%

Most occurring characters

ValueCountFrequency (%)
0 1791812
66.7%
. 895906
33.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2687718
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1791812
66.7%
. 895906
33.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2687718
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1791812
66.7%
. 895906
33.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2687718
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1791812
66.7%
. 895906
33.3%

CRASH_HOUR
Real number (ℝ)

Zeros 

Distinct24
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.201156
Minimum0
Maximum23
Zeros19577
Zeros (%)2.2%
Negative0
Negative (%)0.0%
Memory size6.9 MiB
2024-12-04T12:58:12.637595image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q19
median14
Q317
95-th percentile22
Maximum23
Range23
Interquartile range (IQR)8

Descriptive statistics

Standard deviation5.5721761
Coefficient of variation (CV)0.42209758
Kurtosis-0.38820372
Mean13.201156
Median Absolute Deviation (MAD)4
Skewness-0.42849832
Sum11853054
Variance31.049146
MonotonicityNot monotonic
2024-12-04T12:58:12.754596image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
15 69528
 
7.7%
16 68733
 
7.7%
17 66851
 
7.4%
14 59971
 
6.7%
18 55165
 
6.1%
13 54308
 
6.0%
12 52604
 
5.9%
8 47410
 
5.3%
11 45551
 
5.1%
9 41042
 
4.6%
Other values (14) 336717
37.5%
ValueCountFrequency (%)
0 19577
2.2%
1 16709
 
1.9%
2 14307
 
1.6%
3 11805
 
1.3%
4 10432
 
1.2%
5 12342
 
1.4%
6 19408
2.2%
7 38012
4.2%
8 47410
5.3%
9 41042
4.6%
ValueCountFrequency (%)
23 23425
 
2.6%
22 27025
 
3.0%
21 29347
3.3%
20 32890
3.7%
19 40656
4.5%
18 55165
6.1%
17 66851
7.4%
16 68733
7.7%
15 69528
7.7%
14 59971
6.7%

CRASH_DAY_OF_WEEK
Real number (ℝ)

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.120603
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.9 MiB
2024-12-04T12:58:12.855596image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median4
Q36
95-th percentile7
Maximum7
Range6
Interquartile range (IQR)4

Descriptive statistics

Standard deviation1.9808583
Coefficient of variation (CV)0.48072049
Kurtosis-1.2386448
Mean4.120603
Median Absolute Deviation (MAD)2
Skewness-0.076857429
Sum3699807
Variance3.9237996
MonotonicityNot monotonic
2024-12-04T12:58:13.006596image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
6 145517
16.2%
7 132842
14.8%
5 129141
14.4%
3 127875
14.2%
4 127305
14.2%
2 123061
13.7%
1 112139
12.5%
ValueCountFrequency (%)
1 112139
12.5%
2 123061
13.7%
3 127875
14.2%
4 127305
14.2%
5 129141
14.4%
6 145517
16.2%
7 132842
14.8%
ValueCountFrequency (%)
7 132842
14.8%
6 145517
16.2%
5 129141
14.4%
4 127305
14.2%
3 127875
14.2%
2 123061
13.7%
1 112139
12.5%

CRASH_MONTH
Real number (ℝ)

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.7181483
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.9 MiB
2024-12-04T12:58:13.131596image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median7
Q310
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.3741588
Coefficient of variation (CV)0.50224535
Kurtosis-1.1589333
Mean6.7181483
Median Absolute Deviation (MAD)3
Skewness-0.10574809
Sum6032091
Variance11.384947
MonotonicityNot monotonic
2024-12-04T12:58:13.239595image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
10 86677
9.7%
9 82225
9.2%
8 80820
9.0%
7 78568
8.8%
11 78039
8.7%
6 77697
8.7%
5 77268
8.6%
12 71006
7.9%
3 67811
7.6%
4 66417
7.4%
Other values (2) 131352
14.6%
ValueCountFrequency (%)
1 66068
7.4%
2 65284
7.3%
3 67811
7.6%
4 66417
7.4%
5 77268
8.6%
6 77697
8.7%
7 78568
8.8%
8 80820
9.0%
9 82225
9.2%
10 86677
9.7%
ValueCountFrequency (%)
12 71006
7.9%
11 78039
8.7%
10 86677
9.7%
9 82225
9.2%
8 80820
9.0%
7 78568
8.8%
6 77697
8.7%
5 77268
8.6%
4 66417
7.4%
3 67811
7.6%

LATITUDE
Real number (ℝ)

High correlation  Skewed 

Distinct318580
Distinct (%)35.7%
Missing6482
Missing (%)0.7%
Infinite0
Infinite (%)0.0%
Mean41.855147
Minimum0
Maximum42.02278
Zeros54
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size6.9 MiB
2024-12-04T12:58:13.366596image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile41.712771
Q141.783272
median41.874979
Q341.924572
95-th percentile41.990161
Maximum42.02278
Range42.02278
Interquartile range (IQR)0.14129935

Descriptive statistics

Standard deviation0.33697448
Coefficient of variation (CV)0.0080509687
Kurtosis14415.936
Mean41.855147
Median Absolute Deviation (MAD)0.068241591
Skewness-116.08825
Sum37309594
Variance0.1135518
MonotonicityNot monotonic
2024-12-04T12:58:13.504595image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
41.97620114 1432
 
0.2%
41.90095892 815
 
0.1%
41.79142028 625
 
0.1%
41.7514606 611
 
0.1%
41.72225727 487
 
0.1%
41.75466012 408
 
< 0.1%
41.88085605 366
 
< 0.1%
41.78932932 351
 
< 0.1%
41.90075297 339
 
< 0.1%
41.89680497 338
 
< 0.1%
Other values (318570) 885626
98.6%
(Missing) 6482
 
0.7%
ValueCountFrequency (%)
0 54
< 0.1%
41.64467013 26
< 0.1%
41.64469152 5
 
< 0.1%
41.64469397 7
 
< 0.1%
41.64469408 1
 
< 0.1%
41.64469775 1
 
< 0.1%
41.64470194 6
 
< 0.1%
41.64471103 1
 
< 0.1%
41.64471232 2
 
< 0.1%
41.64471457 1
 
< 0.1%
ValueCountFrequency (%)
42.02277986 10
< 0.1%
42.02275469 1
 
< 0.1%
42.02273632 1
 
< 0.1%
42.02272017 2
 
< 0.1%
42.02266893 1
 
< 0.1%
42.02266114 2
 
< 0.1%
42.02266027 9
< 0.1%
42.02265996 1
 
< 0.1%
42.02265785 1
 
< 0.1%
42.0226457 1
 
< 0.1%

LONGITUDE
Real number (ℝ)

High correlation  Skewed 

Distinct318545
Distinct (%)35.7%
Missing6482
Missing (%)0.7%
Infinite0
Infinite (%)0.0%
Mean-87.673638
Minimum-87.936193
Maximum0
Zeros54
Zeros (%)< 0.1%
Negative891344
Negative (%)99.3%
Memory size6.9 MiB
2024-12-04T12:58:13.669595image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Quantile statistics

Minimum-87.936193
5-th percentile-87.777028
Q1-87.721831
median-87.674283
Q3-87.633625
95-th percentile-87.585813
Maximum0
Range87.936193
Interquartile range (IQR)0.088206134

Descriptive statistics

Standard deviation0.68495344
Coefficient of variation (CV)-0.0078125358
Kurtosis16258.381
Mean-87.673638
Median Absolute Deviation (MAD)0.043142945
Skewness127.0417
Sum-78152106
Variance0.46916121
MonotonicityNot monotonic
2024-12-04T12:58:13.824596image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-87.90530913 1432
 
0.2%
-87.61992817 815
 
0.1%
-87.58014777 625
 
0.1%
-87.58597199 611
 
0.1%
-87.58527557 487
 
0.1%
-87.74138476 408
 
< 0.1%
-87.61763589 366
 
< 0.1%
-87.74164564 351
 
< 0.1%
-87.624235 339
 
< 0.1%
-87.61702742 338
 
< 0.1%
Other values (318535) 885626
98.6%
(Missing) 6482
 
0.7%
ValueCountFrequency (%)
-87.93619295 1
 
< 0.1%
-87.93587692 1
 
< 0.1%
-87.93476313 3
 
< 0.1%
-87.93450972 1
 
< 0.1%
-87.93401422 1
 
< 0.1%
-87.93399393 54
< 0.1%
-87.9339765 3
 
< 0.1%
-87.93302828 8
 
< 0.1%
-87.92822117 3
 
< 0.1%
-87.92726168 13
 
< 0.1%
ValueCountFrequency (%)
0 54
< 0.1%
-87.52458739 15
 
< 0.1%
-87.52458901 4
 
< 0.1%
-87.52464032 1
 
< 0.1%
-87.5246459 1
 
< 0.1%
-87.52467395 8
 
< 0.1%
-87.52467489 1
 
< 0.1%
-87.52467584 1
 
< 0.1%
-87.52467708 1
 
< 0.1%
-87.52468243 1
 
< 0.1%
Distinct318782
Distinct (%)35.8%
Missing6482
Missing (%)0.7%
Memory size6.9 MiB
2024-12-04T12:58:14.335596image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Length

Max length40
Median length40
Mean length39.779494
Min length11

Characters and Unicode

Total characters35459361
Distinct characters20
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique198764 ?
Unique (%)22.3%

Sample

1st rowPOINT (-87.665902342962 41.854120262952)
2nd rowPOINT (-87.594212812011 41.809781151018)
3rd rowPOINT (-87.696642374961 41.899224596015)
4th rowPOINT (-87.709134319958 41.975852858025)
5th rowPOINT (-87.626521907009 41.758245504966)
ValueCountFrequency (%)
point 891398
33.3%
87.905309125103 1432
 
0.1%
41.976201139024 1432
 
0.1%
87.619928173678 815
 
< 0.1%
41.900958919109 815
 
< 0.1%
87.580147768689 625
 
< 0.1%
41.791420282098 625
 
< 0.1%
41.751460603167 611
 
< 0.1%
87.585971992965 611
 
< 0.1%
41.722257273006 487
 
< 0.1%
Other values (637554) 1775343
66.4%
2024-12-04T12:58:14.988596image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
7 3389619
 
9.6%
8 3175006
 
9.0%
4 2853968
 
8.0%
1 2793530
 
7.9%
6 2532721
 
7.1%
9 2247812
 
6.3%
5 2045544
 
5.8%
2 2001746
 
5.6%
3 1959080
 
5.5%
1782796
 
5.0%
Other values (10) 10677539
30.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 35459361
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
7 3389619
 
9.6%
8 3175006
 
9.0%
4 2853968
 
8.0%
1 2793530
 
7.9%
6 2532721
 
7.1%
9 2247812
 
6.3%
5 2045544
 
5.8%
2 2001746
 
5.6%
3 1959080
 
5.5%
1782796
 
5.0%
Other values (10) 10677539
30.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 35459361
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
7 3389619
 
9.6%
8 3175006
 
9.0%
4 2853968
 
8.0%
1 2793530
 
7.9%
6 2532721
 
7.1%
9 2247812
 
6.3%
5 2045544
 
5.8%
2 2001746
 
5.6%
3 1959080
 
5.5%
1782796
 
5.0%
Other values (10) 10677539
30.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 35459361
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
7 3389619
 
9.6%
8 3175006
 
9.0%
4 2853968
 
8.0%
1 2793530
 
7.9%
6 2532721
 
7.1%
9 2247812
 
6.3%
5 2045544
 
5.8%
2 2001746
 
5.6%
3 1959080
 
5.5%
1782796
 
5.0%
Other values (10) 10677539
30.1%

Interactions

2024-12-04T12:57:42.134908image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:56:57.849632image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:57:01.075397image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:57:03.135346image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:57:06.510982image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:57:09.967069image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:57:13.260598image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:57:16.507788image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:57:19.780900image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:57:22.827332image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:57:26.093651image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:57:29.467188image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:57:32.641787image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:57:35.970614image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:57:39.216636image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:57:42.257901image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:56:57.999733image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:57:01.205718image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:57:03.262344image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:57:06.658273image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:57:10.108133image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:57:13.383599image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:57:16.641790image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:57:19.913942image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:57:22.958335image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:57:26.213651image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:57:29.602777image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:57:32.776785image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:57:36.102615image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:57:39.347636image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:57:42.519907image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:56:58.219733image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:57:01.336718image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:57:03.466342image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:57:06.880273image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:57:10.325451image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:57:13.632598image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:57:17.004797image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:57:20.121687image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:57:23.166651image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:57:26.446662image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:57:29.837781image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:57:33.019222image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:57:36.344429image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:57:39.545763image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:57:42.734908image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:56:58.440733image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:57:01.467763image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:57:03.680344image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:57:07.243525image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:57:10.573453image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:57:13.831599image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:57:17.214793image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:57:20.321677image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:57:23.368650image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:57:26.669652image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:57:30.066174image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:57:33.238210image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:57:36.571430image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:57:39.760763image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:57:42.937901image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:56:58.677285image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:57:01.590834image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:57:03.925350image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:57:07.492660image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:57:10.853456image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:57:14.041280image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:57:17.436788image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:57:20.543684image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:57:23.577651image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:57:26.926650image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:57:30.292761image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:57:33.452364image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:57:36.805430image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:57:39.960761image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:57:43.163097image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:56:58.908286image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:57:01.731838image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:57:04.177369image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:57:07.734649image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:57:11.092452image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:57:14.234788image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:57:17.656789image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:57:20.771677image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:57:23.794660image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:57:27.157653image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:57:30.512559image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:57:33.667128image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:57:37.046428image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:57:40.192313image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:57:43.360097image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:56:59.144066image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:57:01.857834image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:57:04.392374image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:57:07.987651image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:57:11.312862image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:57:14.433787image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:57:17.849788image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:57:20.973320image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:57:24.002651image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:57:27.377670image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:57:30.725234image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:57:33.867726image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:57:37.262683image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:57:40.385629image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:57:43.567097image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:56:59.362064image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:57:02.008840image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:57:04.616365image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:57:08.226082image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:57:11.529864image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:57:14.667788image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:57:18.078816image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:57:21.177320image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:57:24.207651image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:57:27.606598image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:57:30.966233image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:57:34.153753image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:57:37.480893image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:57:40.580510image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:57:43.760115image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:56:59.587061image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:57:02.135846image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:57:04.825349image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:57:08.441078image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:57:11.763863image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:57:14.931793image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:57:18.325794image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:57:21.398545image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:57:24.424651image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:57:27.832603image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:57:31.172233image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:57:34.361746image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:57:37.697910image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:57:40.774512image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:57:43.964097image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:56:59.820057image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:57:02.276848image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:57:05.079342image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:57:08.676486image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:57:11.989522image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:57:15.249788image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:57:18.540795image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:57:21.621743image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:57:24.657650image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:57:28.055720image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:57:31.378569image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:57:34.576739image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:57:37.943893image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:57:40.977807image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:57:44.265097image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:57:00.039386image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:57:02.404319image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:57:05.292342image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:57:08.900488image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:57:12.209599image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:57:15.458827image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:57:18.763925image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:57:21.827868image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:57:24.881651image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:57:28.280728image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:57:31.577384image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:57:34.789739image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:57:38.156893image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:57:41.172894image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:57:44.456097image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:57:00.275376image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:57:02.537695image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:57:05.502355image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:57:09.110532image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:57:12.407596image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:57:15.677796image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:57:18.955967image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:57:22.017206image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:57:25.105660image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:57:28.509719image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:57:31.787386image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:57:35.087740image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:57:38.362896image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:57:41.363894image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:57:44.662010image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:57:00.501387image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:57:02.667692image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:57:05.769343image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:57:09.326659image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:57:12.640599image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:57:15.886788image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:57:19.175902image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:57:22.216773image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:57:25.318651image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:57:28.726406image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:57:32.019496image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:57:35.326064image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:57:38.627636image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:57:41.552909image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:57:44.880002image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:57:00.726401image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:57:02.786702image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:57:05.996012image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:57:09.556660image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:57:12.851599image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:57:16.093788image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:57:19.383902image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:57:22.409335image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:57:25.526651image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:57:29.051222image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:57:32.224789image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:57:35.541624image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:57:38.830636image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:57:41.738894image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:57:45.077019image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:57:00.938398image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:57:02.908701image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:57:06.259980image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:57:09.775663image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:57:13.054598image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:57:16.310788image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:57:19.575902image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:57:22.619335image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:57:25.731666image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:57:29.256222image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:57:32.425787image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:57:35.756614image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:57:39.025645image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T12:57:41.931895image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Correlations

2024-12-04T12:58:15.249596image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
ALIGNMENTBEAT_OF_OCCURRENCECRASH_DATE_EST_ICRASH_DAY_OF_WEEKCRASH_HOURCRASH_MONTHCRASH_TYPEDAMAGEDEVICE_CONDITIONDOORING_IFIRST_CRASH_TYPEHIT_AND_RUN_IINJURIES_FATALINJURIES_INCAPACITATINGINJURIES_NON_INCAPACITATINGINJURIES_NO_INDICATIONINJURIES_REPORTED_NOT_EVIDENTINJURIES_TOTALINTERSECTION_RELATED_ILANE_CNTLATITUDELIGHTING_CONDITIONLONGITUDEMOST_SEVERE_INJURYNOT_RIGHT_OF_WAY_INUM_UNITSPHOTOS_TAKEN_IPOSTED_SPEED_LIMITPRIM_CONTRIBUTORY_CAUSEREPORT_TYPEROADWAY_SURFACE_CONDROAD_DEFECTSEC_CONTRIBUTORY_CAUSESTATEMENTS_TAKEN_ISTREET_DIRECTIONSTREET_NOTRAFFICWAY_TYPETRAFFIC_CONTROL_DEVICEWEATHER_CONDITIONWORKERS_PRESENT_IWORK_ZONE_IWORK_ZONE_TYPE
ALIGNMENT1.0000.0280.0150.0060.0190.0050.0530.0160.0130.0090.0580.0070.0050.0050.0000.0000.0020.0040.0140.0000.0000.0150.0000.0120.0000.0040.0000.0680.0410.0450.0270.0230.0270.0000.0260.0000.0900.0320.0220.0000.0530.000
BEAT_OF_OCCURRENCE0.0281.0000.0300.0040.0040.0020.0490.0410.0300.2650.0440.0200.005-0.014-0.026-0.006-0.016-0.0320.010-0.0540.6610.024-0.5020.0180.0390.0230.057-0.0580.0520.0310.0320.0270.0380.0490.4340.0090.0700.0410.0230.0000.0330.026
CRASH_DATE_EST_I0.0150.0301.0000.0000.0310.0090.0910.0340.0520.0820.0810.0370.0090.0100.0090.0100.0160.0170.0281.0000.0000.1090.0000.0790.0810.0460.1360.0320.0840.1150.0800.0190.0550.1490.0151.0000.0700.0530.0930.1330.2060.050
CRASH_DAY_OF_WEEK0.0060.0040.0001.0000.056-0.0020.0480.0260.0070.0000.0310.0000.003-0.002-0.0060.025-0.000-0.0050.0020.0100.0030.0630.0050.0090.0080.0040.0170.0080.0260.0230.0120.0040.0140.0000.005-0.0100.0130.0090.0160.3030.0980.029
CRASH_HOUR0.0190.0040.0310.0561.0000.0030.1580.0670.0210.1770.0710.0130.0150.0020.0080.0860.0050.0100.0170.0140.0040.3800.0140.0310.0790.0210.0450.0240.0630.1030.0350.0260.0350.0410.018-0.0030.0380.0200.0380.3220.1210.056
CRASH_MONTH0.0050.0020.009-0.0020.0031.0000.0160.0060.0060.1100.0240.0000.0010.0050.0120.0020.0060.0140.0060.0090.0040.076-0.0030.0170.0000.0110.0410.0090.0360.0060.1440.0130.0240.0230.0060.0000.0090.0070.1010.0880.0520.037
CRASH_TYPE0.0530.0490.0910.0480.1580.0161.0000.2430.1150.2060.3630.0650.0550.0750.1230.0090.1730.1770.0390.0000.0000.1630.0000.6680.0950.2130.0600.1110.2970.4940.1130.0630.1620.0320.0270.0000.2130.1300.1020.0370.0590.020
DAMAGE0.0160.0410.0340.0260.0670.0060.2431.0000.0290.1540.2190.0210.0110.0200.0330.0080.0320.0450.0250.0030.0030.0560.0030.0920.0400.0860.0730.0430.1030.1320.0260.0240.0550.0230.0250.0000.0790.0390.0190.0000.0360.000
DEVICE_CONDITION0.0130.0300.0520.0070.0210.0060.1150.0291.0000.2390.1600.0200.0000.0070.0140.0080.0170.0200.1600.0120.0050.1310.0050.0640.1110.0080.0280.0820.1220.0680.1420.1620.0620.0140.0200.0000.1700.4670.1180.0690.0690.056
DOORING_I0.0090.2650.0820.0000.1770.1100.2060.1540.2391.0000.5370.0000.0250.0150.0000.0000.0370.0540.0001.0001.0000.1281.0000.2620.0000.0620.0000.0940.2820.0070.0800.0160.2100.0550.2551.0000.2530.2670.069NaN0.0000.000
FIRST_CRASH_TYPE0.0580.0440.0810.0310.0710.0240.3630.2190.1600.5371.0000.1190.0330.0180.0260.0140.0280.0340.1730.0000.0050.0950.0050.2320.1040.0460.0380.1160.2780.1790.0610.1340.1370.0560.0410.0000.1380.1360.0470.0870.2120.087
HIT_AND_RUN_I0.0070.0200.0370.0000.0130.0000.0650.0210.0200.0000.1191.0000.0040.0170.0210.0100.0110.0260.0220.0000.0000.0280.0000.0400.0600.0270.0260.0140.1030.0350.0240.0070.0520.0420.0171.0000.0330.0200.0240.0000.1570.000
INJURIES_FATAL0.0050.0050.0090.0030.0150.0010.0550.0110.0000.0250.0330.0041.0000.0340.0270.0000.0030.1070.0040.0000.0000.0120.0000.5000.0150.0140.0460.0050.0250.0220.0030.0020.0130.0040.0050.0000.0080.0030.0020.0000.0340.000
INJURIES_INCAPACITATING0.005-0.0140.010-0.0020.0020.0050.0750.0200.0070.0150.0180.0170.0341.0000.043-0.1110.0040.3270.0000.020-0.0110.0090.0040.1710.0050.0210.0160.0310.0190.0300.0050.0020.0160.0050.0040.0040.0100.0060.0020.0200.0000.000
INJURIES_NON_INCAPACITATING0.000-0.0260.009-0.0060.0080.0120.1230.0330.0140.0000.0260.0210.0270.0431.000-0.2180.0050.7430.0080.047-0.0280.0120.0140.1200.0150.0580.0180.0610.0250.0490.0070.0030.0150.0160.0110.0040.0170.0130.0050.0000.0120.000
INJURIES_NO_INDICATION0.000-0.0060.0100.0250.0860.0020.0090.0080.0080.0000.0140.0100.000-0.111-0.2181.000-0.125-0.2740.0000.2200.0150.006-0.0000.0020.0170.2120.0000.1380.0150.0240.0040.0040.0100.0000.004-0.0470.0070.0090.0040.0000.0000.009
INJURIES_REPORTED_NOT_EVIDENT0.002-0.0160.016-0.0000.0050.0060.1730.0320.0170.0370.0280.0110.0030.0040.005-0.1251.0000.5640.0100.047-0.0260.0090.0080.2180.0210.0600.0000.0470.0220.0450.0080.0050.0160.0000.0090.0030.0190.0170.0080.0000.0000.015
INJURIES_TOTAL0.004-0.0320.017-0.0050.0100.0140.1770.0450.0200.0540.0340.0260.1070.3270.743-0.2740.5641.0000.0130.068-0.0390.0160.0160.1390.0230.0770.0000.0820.0330.0650.0110.0060.0200.0140.0140.0060.0240.0180.0080.0000.0180.012
INTERSECTION_RELATED_I0.0140.0100.0280.0020.0170.0060.0390.0250.1600.0000.1730.0220.0040.0000.0080.0000.0100.0131.0000.0000.0000.0180.0000.0320.0420.0180.1310.0380.0960.0060.0110.0130.0500.1120.0101.0000.1320.1900.0120.0000.1610.066
LANE_CNT0.000-0.0541.0000.0100.0140.0090.0000.0030.0121.0000.0000.0000.0000.0200.0470.2200.0470.0680.0001.000-0.0090.0040.0760.0001.0000.0321.0000.3200.0000.0000.0000.0000.0001.0000.000-0.0550.0000.0130.0001.0001.0001.000
LATITUDE0.0000.6610.0000.0030.0040.0040.0000.0030.0051.0000.0050.0000.000-0.011-0.0280.015-0.026-0.0390.000-0.0091.0000.002-0.4620.0021.0000.0201.000-0.0310.0000.0000.0000.0000.0061.0000.009-0.1670.0040.0070.0001.0001.0001.000
LIGHTING_CONDITION0.0150.0240.1090.0630.3800.0760.1630.0560.1310.1280.0950.0280.0120.0090.0120.0060.0090.0160.0180.0040.0021.0000.0020.0440.0760.0350.0150.0310.1010.1150.2450.1330.0570.0300.0190.0000.1060.1370.3050.2580.1120.040
LONGITUDE0.000-0.5020.0000.0050.014-0.0030.0000.0030.0051.0000.0050.0000.0000.0040.014-0.0000.0080.0160.0000.076-0.4620.0021.0000.0021.000-0.0461.0000.0570.0000.0000.0000.0000.0061.0000.009-0.1920.0040.0070.0001.0001.0001.000
MOST_SEVERE_INJURY0.0120.0180.0790.0090.0310.0170.6680.0920.0640.2620.2320.0400.5000.1710.1200.0020.2180.1390.0320.0000.0020.0440.0021.0000.0680.0590.0580.0410.1150.2090.0360.0180.0620.0230.0120.0000.0850.0700.0350.0000.0630.011
NOT_RIGHT_OF_WAY_I0.0000.0390.0810.0080.0790.0000.0950.0400.1110.0000.1040.0600.0150.0050.0150.0170.0210.0230.0421.0001.0000.0761.0000.0681.0000.0520.2240.1510.0930.0790.0270.0110.0550.1980.0141.0000.2370.1480.0320.0000.1670.000
NUM_UNITS0.0040.0230.0460.0040.0210.0110.2130.0860.0080.0620.0460.0270.0140.0210.0580.2120.0600.0770.0180.0320.0200.035-0.0460.0590.0521.0000.0480.0390.0550.1270.0180.0140.0310.0300.0060.0120.0240.0100.0150.0000.0590.000
PHOTOS_TAKEN_I0.0000.0570.1360.0170.0450.0410.0600.0730.0280.0000.0380.0260.0460.0160.0180.0000.0000.0000.1311.0001.0000.0151.0000.0580.2240.0481.0000.0220.0650.1600.0000.0230.0320.6140.0121.0000.0490.0390.0180.0000.2650.000
POSTED_SPEED_LIMIT0.068-0.0580.0320.0080.0240.0090.1110.0430.0820.0940.1160.0140.0050.0310.0610.1380.0470.0820.0380.320-0.0310.0310.0570.0410.1510.0390.0221.0000.0830.0410.0240.0180.0420.0150.060-0.0170.2560.0890.0200.0900.0920.049
PRIM_CONTRIBUTORY_CAUSE0.0410.0520.0840.0260.0630.0360.2970.1030.1220.2820.2780.1030.0250.0190.0250.0150.0220.0330.0960.0000.0000.1010.0000.1150.0930.0550.0650.0831.0000.1770.1610.1860.1970.0670.0470.0000.0930.1150.1070.1720.1940.046
REPORT_TYPE0.0450.0310.1150.0230.1030.0060.4940.1320.0680.0070.1790.0350.0220.0300.0490.0240.0450.0650.0060.0000.0000.1150.0000.2090.0790.1270.1600.0410.1771.0000.1290.0930.1050.0190.0140.0000.1260.0790.1130.0670.0190.142
ROADWAY_SURFACE_COND0.0270.0320.0800.0120.0350.1440.1130.0260.1420.0800.0610.0240.0030.0050.0070.0040.0080.0110.0110.0000.0000.2450.0000.0360.0270.0180.0000.0240.1610.1291.0000.2260.1100.0110.0230.0010.0840.1270.5170.0440.0750.049
ROAD_DEFECT0.0230.0270.0190.0040.0260.0130.0630.0240.1620.0160.1340.0070.0020.0020.0030.0040.0050.0060.0130.0000.0000.1330.0000.0180.0110.0140.0230.0180.1860.0930.2261.0000.1020.0000.0150.0000.0710.1260.1500.0340.2430.066
SEC_CONTRIBUTORY_CAUSE0.0270.0380.0550.0140.0350.0240.1620.0550.0620.2100.1370.0520.0130.0160.0150.0100.0160.0200.0500.0000.0060.0570.0060.0620.0550.0310.0320.0420.1970.1050.1100.1021.0000.0060.0240.0000.0460.0590.0800.1260.1240.028
STATEMENTS_TAKEN_I0.0000.0490.1490.0000.0410.0230.0320.0230.0140.0550.0560.0420.0040.0050.0160.0000.0000.0140.1121.0001.0000.0301.0000.0230.1980.0300.6140.0150.0670.0190.0110.0000.0061.0000.0161.0000.0430.0270.0190.0000.2230.102
STREET_DIRECTION0.0260.4340.0150.0050.0180.0060.0270.0250.0200.2550.0410.0170.0050.0040.0110.0040.0090.0140.0100.0000.0090.0190.0090.0120.0140.0060.0120.0600.0470.0140.0230.0150.0240.0161.0000.0000.0600.0290.0180.0000.0000.000
STREET_NO0.0000.0091.000-0.010-0.0030.0000.0000.0000.0001.0000.0001.0000.0000.0040.004-0.0470.0030.0061.000-0.055-0.1670.000-0.1920.0001.0000.0121.000-0.0170.0000.0000.0010.0000.0001.0000.0001.0000.0000.0000.0001.0001.0001.000
TRAFFICWAY_TYPE0.0900.0700.0700.0130.0380.0090.2130.0790.1700.2530.1380.0330.0080.0100.0170.0070.0190.0240.1320.0000.0040.1060.0040.0850.2370.0240.0490.2560.0930.1260.0840.0710.0460.0430.0600.0001.0000.1230.0640.0730.2090.057
TRAFFIC_CONTROL_DEVICE0.0320.0410.0530.0090.0200.0070.1300.0390.4670.2670.1360.0200.0030.0060.0130.0090.0170.0180.1900.0130.0070.1370.0070.0700.1480.0100.0390.0890.1150.0790.1270.1260.0590.0270.0290.0000.1231.0000.0950.0850.1350.060
WEATHER_CONDITION0.0220.0230.0930.0160.0380.1010.1020.0190.1180.0690.0470.0240.0020.0020.0050.0040.0080.0080.0120.0000.0000.3050.0000.0350.0320.0150.0180.0200.1070.1130.5170.1500.0800.0190.0180.0000.0640.0951.0000.0000.0390.057
WORKERS_PRESENT_I0.0000.0000.1330.3030.3220.0880.0370.0000.069NaN0.0870.0000.0000.0200.0000.0000.0000.0000.0001.0001.0000.2581.0000.0000.0000.0000.0000.0900.1720.0670.0440.0340.1260.0000.0001.0000.0730.0850.0001.0001.0000.076
WORK_ZONE_I0.0530.0330.2060.0980.1210.0520.0590.0360.0690.0000.2120.1570.0340.0000.0120.0000.0000.0180.1611.0001.0000.1121.0000.0630.1670.0590.2650.0920.1940.0190.0750.2430.1240.2230.0001.0000.2090.1350.0391.0001.0001.000
WORK_ZONE_TYPE0.0000.0260.0500.0290.0560.0370.0200.0000.0560.0000.0870.0000.0000.0000.0000.0090.0150.0120.0661.0001.0000.0401.0000.0110.0000.0000.0000.0490.0460.1420.0490.0660.0280.1020.0001.0000.0570.0600.0570.0761.0001.000

Missing values

2024-12-04T12:57:46.091892image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
A simple visualization of nullity by column.
2024-12-04T12:57:49.523171image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-12-04T12:57:57.900448image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

CRASH_RECORD_IDCRASH_DATE_EST_ICRASH_DATEPOSTED_SPEED_LIMITTRAFFIC_CONTROL_DEVICEDEVICE_CONDITIONWEATHER_CONDITIONLIGHTING_CONDITIONFIRST_CRASH_TYPETRAFFICWAY_TYPELANE_CNTALIGNMENTROADWAY_SURFACE_CONDROAD_DEFECTREPORT_TYPECRASH_TYPEINTERSECTION_RELATED_INOT_RIGHT_OF_WAY_IHIT_AND_RUN_IDAMAGEDATE_POLICE_NOTIFIEDPRIM_CONTRIBUTORY_CAUSESEC_CONTRIBUTORY_CAUSESTREET_NOSTREET_DIRECTIONSTREET_NAMEBEAT_OF_OCCURRENCEPHOTOS_TAKEN_ISTATEMENTS_TAKEN_IDOORING_IWORK_ZONE_IWORK_ZONE_TYPEWORKERS_PRESENT_INUM_UNITSMOST_SEVERE_INJURYINJURIES_TOTALINJURIES_FATALINJURIES_INCAPACITATINGINJURIES_NON_INCAPACITATINGINJURIES_REPORTED_NOT_EVIDENTINJURIES_NO_INDICATIONINJURIES_UNKNOWNCRASH_HOURCRASH_DAY_OF_WEEKCRASH_MONTHLATITUDELONGITUDELOCATION
023a79931ef555d54118f64dc9be2cf2dbf59636ce253f7a1179c4a1c091442a6eeab8352220c7c56ca1ff7c4b4b0fc345c74e3e85ecb9d43deeb66b5f803d4a0NaN09/05/2023 07:05:00 PM30TRAFFIC SIGNALFUNCTIONING PROPERLYCLEARDUSKANGLEFIVE POINT, OR MORENaNSTRAIGHT AND LEVELDRYNO DEFECTSON SCENEINJURY AND / OR TOW DUE TO CRASHYNaNNaNOVER $1,50009/05/2023 07:05:00 PMUNABLE TO DETERMINENOT APPLICABLE5500SWENTWORTH AVE225.0NaNNaNNaNNaNNaNNaN2INCAPACITATING INJURY3.00.01.02.00.02.00.01939NaNNaNNaN
12675c13fd0f474d730a5b780968b3cafc7c12d7adb661fa8a3093c0658d5a0d51b720fc9e031a1ddd83c761a8e2aa7283573557db246f4c9e956aaa58719cacfNaN09/22/2023 06:45:00 PM50NO CONTROLSNO CONTROLSCLEARDARKNESS, LIGHTED ROADREAR ENDDIVIDED - W/MEDIAN BARRIERNaNSTRAIGHT AND LEVELDRYNO DEFECTSON SCENENO INJURY / DRIVE AWAYNaNNaNNaNOVER $1,50009/22/2023 06:50:00 PMFOLLOWING TOO CLOSELYFOLLOWING TOO CLOSELY7900SCHICAGO SKYWAY OB411.0NaNNaNNaNNaNNaNNaN2NO INDICATION OF INJURY0.00.00.00.00.02.00.01869NaNNaNNaN
25f54a59fcb087b12ae5b1acff96a3caf4f2d37e79f8db4106558b34b8a6d2b81af02cf91b576ecd7ced08ffd10fcfd940a84f7613125b89d33636e6075064e22NaN07/29/2023 02:45:00 PM30TRAFFIC SIGNALFUNCTIONING PROPERLYCLEARDAYLIGHTPARKED MOTOR VEHICLEDIVIDED - W/MEDIAN (NOT RAISED)NaNSTRAIGHT AND LEVELDRYNO DEFECTSON SCENENO INJURY / DRIVE AWAYNaNNaNYOVER $1,50007/29/2023 02:45:00 PMFAILING TO REDUCE SPEED TO AVOID CRASHOPERATING VEHICLE IN ERRATIC, RECKLESS, CARELESS, NEGLIGENT OR AGGRESSIVE MANNER2101SASHLAND AVE1235.0NaNNaNNaNNaNNaNNaN4NO INDICATION OF INJURY0.00.00.00.00.01.00.0147741.85412-87.665902POINT (-87.665902342962 41.854120262952)
37ebf015016f83d09b321afd671a836d6b148330535d5df85f232edb575a7f2a42e61b9747067e89c4e7a73e69efc819c9003ed153e19765f2ecc6f7b2421c98dNaN08/09/2023 11:00:00 PM30NO CONTROLSNO CONTROLSCLEARDARKNESS, LIGHTED ROADSIDESWIPE SAME DIRECTIONNOT DIVIDEDNaNSTRAIGHT AND LEVELDRYNO DEFECTSON SCENENO INJURY / DRIVE AWAYNaNNaNNaNOVER $1,50008/09/2023 11:40:00 PMFAILING TO YIELD RIGHT-OF-WAYNOT APPLICABLE10020WBALMORAL AVE1650.0NaNNaNNaNNaNNaNNaN2NO INDICATION OF INJURY0.00.00.00.00.02.00.02348NaNNaNNaN
46c1659069e9c6285a650e70d6f9b574ed5f64c12888479093dfeef179c0344ec6d2057eae224b5c0d5dfc278c0a237f8c22543f07fdef2e4a95a3849871c9345NaN08/18/2023 12:50:00 PM15OTHERFUNCTIONING PROPERLYCLEARDAYLIGHTREAR ENDOTHERNaNSTRAIGHT AND LEVELDRYNO DEFECTSON SCENEINJURY AND / OR TOW DUE TO CRASHNaNNaNNaNOVER $1,50008/18/2023 12:55:00 PMFOLLOWING TOO CLOSELYDISTRACTION - FROM INSIDE VEHICLE700WOHARE ST1654.0NaNNaNNaNNaNNaNNaN2NONINCAPACITATING INJURY1.00.00.01.00.01.00.01268NaNNaNNaN
5004cd14d0303a9163aad69a2d7f341b7da2a8572b2ab3378594bfae8ac53dcb604dd8d414f93c290b55862f9f2517ad32e6209cbc8034c2e26eb3c2bc9724390NaN11/26/2019 08:38:00 AM25NO CONTROLSNO CONTROLSCLEARDAYLIGHTPEDESTRIANONE-WAYNaNCURVE ON GRADEDRYNO DEFECTSON SCENEINJURY AND / OR TOW DUE TO CRASHNaNNaNNaNOVER $1,50011/26/2019 08:38:00 AMUNABLE TO DETERMINENOT APPLICABLE5WTERMINAL ST1655.0YYNaNNaNNaNNaN2FATAL1.01.00.00.00.01.00.08311NaNNaNNaN
635156ce97cab22747495e92e8bbb16c57e0e60dc3ce6d1f1852f2f7cece07c7ae825b073b286b1da52dfa58082ff6d763ecf1f13f06a223c7aed2b6c1e8c5972NaN02/06/2023 05:30:00 PM30NO CONTROLSNO CONTROLSCLEARDARKNESS, LIGHTED ROADREAR ENDONE-WAYNaNCURVE, LEVELDRYNO DEFECTSON SCENENO INJURY / DRIVE AWAYNaNNaNNaN$501 - $1,50002/06/2023 05:35:00 PMUNABLE TO DETERMINEUNABLE TO DETERMINE2WTERMINAL ST1652.0NaNNaNNaNNaNNaNNaN2NO INDICATION OF INJURY0.00.00.00.00.02.00.01722NaNNaNNaN
7359bf9f5872d646bb63576e55b1e0b480dc93c2b935ab571dc26ddb48b7a328fbfe130ae70bbff9f03787041b6fb029ba02529da9a1f57494e385ec0e13ed834NaN01/31/2022 07:45:00 PM25NO CONTROLSNO CONTROLSCLEARDARKNESSREAR ENDONE-WAYNaNSTRAIGHT AND LEVELDRYNO DEFECTSNOT ON SCENE (DESK REPORT)NO INJURY / DRIVE AWAYNaNNaNY$501 - $1,50001/31/2022 07:58:00 PMNOT APPLICABLENOT APPLICABLE4546WGLADYS AVE1131.0NaNNaNNaNNaNNaNNaN2NO INDICATION OF INJURY0.00.00.00.00.05.00.01921NaNNaNNaN
836360857c079418cba1b1d70cf653595bbfb4566de8fcb4fff284d6ec2ef9eee82b949759fd83f4ba3df857fed548f769780b036df6ada65aa935b6c669cec53Y01/01/2022 04:32:00 PM10NO CONTROLSNO CONTROLSSNOWDARKNESS, LIGHTED ROADANGLEPARKING LOTNaNSTRAIGHT AND LEVELSNOW OR SLUSHNO DEFECTSON SCENENO INJURY / DRIVE AWAYNaNNaNNaN$501 - $1,50001/01/2022 04:32:00 PMWEATHERNOT APPLICABLE1WPARKING LOT E ST1654.0NaNNaNNaNNaNNaNNaN2NO INDICATION OF INJURY0.00.00.00.00.02.00.01671NaNNaNNaN
937a215843a67b9d2118972242e0ab68232583ffe20401f100ee4de7b8087481f8094ebd7ad6468417477c1b82dec3081b52b429cad17b9954f1ac390e4b5d705NaN10/18/2020 03:58:00 PM35NO CONTROLSNO CONTROLSRAINDAYLIGHTFIXED OBJECTDIVIDED - W/MEDIAN BARRIERNaNCURVE, LEVELWETNO DEFECTSON SCENEINJURY AND / OR TOW DUE TO CRASHNaNNaNNaNOVER $1,50010/18/2020 04:22:00 PMWEATHERNOT APPLICABLE3499E89TH ST424.0NaNNaNNaNNaNNaNNaN1NO INDICATION OF INJURY0.00.00.00.00.01.00.015110NaNNaNNaN
CRASH_RECORD_IDCRASH_DATE_EST_ICRASH_DATEPOSTED_SPEED_LIMITTRAFFIC_CONTROL_DEVICEDEVICE_CONDITIONWEATHER_CONDITIONLIGHTING_CONDITIONFIRST_CRASH_TYPETRAFFICWAY_TYPELANE_CNTALIGNMENTROADWAY_SURFACE_CONDROAD_DEFECTREPORT_TYPECRASH_TYPEINTERSECTION_RELATED_INOT_RIGHT_OF_WAY_IHIT_AND_RUN_IDAMAGEDATE_POLICE_NOTIFIEDPRIM_CONTRIBUTORY_CAUSESEC_CONTRIBUTORY_CAUSESTREET_NOSTREET_DIRECTIONSTREET_NAMEBEAT_OF_OCCURRENCEPHOTOS_TAKEN_ISTATEMENTS_TAKEN_IDOORING_IWORK_ZONE_IWORK_ZONE_TYPEWORKERS_PRESENT_INUM_UNITSMOST_SEVERE_INJURYINJURIES_TOTALINJURIES_FATALINJURIES_INCAPACITATINGINJURIES_NON_INCAPACITATINGINJURIES_REPORTED_NOT_EVIDENTINJURIES_NO_INDICATIONINJURIES_UNKNOWNCRASH_HOURCRASH_DAY_OF_WEEKCRASH_MONTHLATITUDELONGITUDELOCATION
89787061c8dcd63fae60613bc9ec526fa901420cbe99a6d35840052c27bbd0cf1f8d6af74ff575276d3795f26878601232f6b9297b250a3499b62a96373e068134d21aNaN07/10/2023 12:29:00 PM30TRAFFIC SIGNALFUNCTIONING PROPERLYCLEARDAYLIGHTTURNINGFOUR WAYNaNSTRAIGHT AND LEVELDRYNO DEFECTSNaNNO INJURY / DRIVE AWAYNaNNaNNaNOVER $1,50007/10/2023 01:05:00 PMIMPROPER TURNING/NO SIGNALIMPROPER LANE USAGE1800SUNION AVE1235.0NaNNaNNaNNaNNaNNaN2NO INDICATION OF INJURY0.00.00.00.00.02.00.0122741.857531-87.644929POINT (-87.644928607359 41.857530859236)
89787189dc61af34d393db950397f0cc06d53b56d1f5e5fa14d4b6bf57783fc278dc600da7a58f7bf933ab72799e671da8845c115c9c7a57fe4450c54b186b465381f6NaN06/26/2023 04:50:00 PM35NO CONTROLSNO CONTROLSCLEARDAYLIGHTSIDESWIPE SAME DIRECTIONDIVIDED - W/MEDIAN (NOT RAISED)NaNSTRAIGHT AND LEVELDRYNO DEFECTSNaNNO INJURY / DRIVE AWAYNaNNaNNaNOVER $1,50006/26/2023 05:00:00 PMIMPROPER OVERTAKING/PASSINGUNABLE TO DETERMINE8100SHALSTED ST613.0NaNNaNNaNNaNNaNNaN2NO INDICATION OF INJURY0.00.00.00.00.04.00.0162641.746905-87.644077POINT (-87.644077151581 41.746904607442)
897872cea9e897c768f47b97c73685c1a2b3fdcdcd8809a0517aa5acb8f147b012742eb5fa43f4ebd922ad74290ec203174cda0a0c42719a70a9dd8227fe28be988dd4NaN07/05/2019 03:50:00 AM30NO CONTROLSNO CONTROLSCLEARDAYLIGHTREAR TO FRONTPARKING LOTNaNSTRAIGHT AND LEVELDRYNO DEFECTSNaNNO INJURY / DRIVE AWAYNaNNaNNOVER $1,50007/05/2019 09:15:00 AMUNABLE TO DETERMINEUNABLE TO DETERMINE810W59TH ST712.0NaNNaNNaNNaNNaNNaN2NO INDICATION OF INJURY0.00.00.00.00.02.00.036741.787127-87.645488POINT (-87.645487943954 41.787127441561)
89787354d55bfcc6627f587abbe0d14c42e51b812f930566fb06773f93b4402cb25e01bdba2a8e977644f53be3b63f9abca96a80ff6b36f6e0c0c4e3d6f3efaed136a0NaN12/28/2019 01:16:00 AM35UNKNOWNUNKNOWNCLEARDARKNESS, LIGHTED ROADPARKED MOTOR VEHICLEONE-WAYNaNSTRAIGHT AND LEVELDRYNO DEFECTSNaNINJURY AND / OR TOW DUE TO CRASHNaNNaNYOVER $1,50012/28/2019 01:18:00 AMOPERATING VEHICLE IN ERRATIC, RECKLESS, CARELESS, NEGLIGENT OR AGGRESSIVE MANNERNOT APPLICABLE219W115TH ST522.0NaNNaNNaNNaNNaNNaN5NO INDICATION OF INJURY0.00.00.00.00.01.00.0171241.685142-87.628557POINT (-87.628556919131 41.685141540233)
897874376bbadc3c632b81e0185fb1a3ddeed6c0dd52ebb8b3a9a1093a73a7928f85bc7c663699a820d770fe3c336aaec77ee2af2972018742c977525cec44f290bf29NaN10/13/2019 01:40:00 AM30NO CONTROLSNO CONTROLSCLEARDARKNESS, LIGHTED ROADSIDESWIPE SAME DIRECTIONNOT DIVIDEDNaNSTRAIGHT AND LEVELDRYUNKNOWNNaNNO INJURY / DRIVE AWAYNaNNaNN$500 OR LESS10/13/2019 02:50:00 AMFAILING TO YIELD RIGHT-OF-WAYFAILING TO YIELD RIGHT-OF-WAY2357NMILWAUKEE AVE1414.0NaNNaNNaNNaNNaNNaN2NO INDICATION OF INJURY0.00.00.00.00.02.00.0111041.924150-87.699151POINT (-87.699150882692 41.924150305628)
897875f2c0204e5392ff379e5804ad25fad66304949d517327c67a74f5071b98e78e47417ae5043d14ca8e108f22d17b9c4d42d75395d09a80c4ea998da7c058f67e04NaN10/18/2020 12:03:00 AM30NO CONTROLSNO CONTROLSCLEARDARKNESS, LIGHTED ROADSIDESWIPE SAME DIRECTIONNOT DIVIDEDNaNSTRAIGHT AND LEVELDRYNO DEFECTSNaNINJURY AND / OR TOW DUE TO CRASHNaNNaNYOVER $1,50010/18/2020 12:03:00 AMUNABLE TO DETERMINEUNABLE TO DETERMINE6432SKEDZIE AVE823.0NaNNaNNaNNaNNaNNaN2NO INDICATION OF INJURY0.00.00.00.00.04.00.0011041.776184-87.703219POINT (-87.703218946422 41.776183637489)
8978763d00cf22a912d0e18809db862dd67e5812f7b6af1ffa3d6f0679f0b0676d4c706a0db95dd9625492d6772e66e12672fd0bbb726a688668877d9bf24cd0ef9224NaN07/18/2023 02:10:00 PM30UNKNOWNUNKNOWNCLEARDAYLIGHTSIDESWIPE SAME DIRECTIONNOT DIVIDEDNaNSTRAIGHT AND LEVELDRYNO DEFECTSNaNNO INJURY / DRIVE AWAYYNaNNaNOVER $1,50007/18/2023 04:55:00 PMIMPROPER OVERTAKING/PASSINGUNABLE TO DETERMINE4632W63RD ST813.0NaNNaNNaNNaNNaNNaN2NO INDICATION OF INJURY0.00.00.00.00.02.00.0143741.778580-87.738679POINT (-87.738679437114 41.77857996073)
89787715f6eb6cde6a026a007034c7081b1f3fac747fc32685910919e4fc695abf188f7c0bb013db7592c57e13a6486da7f90c1f03c704ae8d447b7123baef0dbb03d1NaN02/17/2018 08:00:00 AM30NO CONTROLSNO CONTROLSCLEARDAYLIGHTPARKED MOTOR VEHICLENOT DIVIDEDNaNSTRAIGHT AND LEVELDRYNO DEFECTSNaNNO INJURY / DRIVE AWAYNaNNaNY$501 - $1,50002/17/2018 08:24:00 AMUNABLE TO DETERMINEUNABLE TO DETERMINE7004NPAULINA ST2423.0NaNNaNNaNNaNNaNNaN2NO INDICATION OF INJURY0.00.00.00.00.01.00.087242.009193-87.672396POINT (-87.672396344055 42.009192992271)
89787837b7acd5f2db6728ca4ac0d7e4217f78b6703f769754b495c437ace10d472977fe4ad4973167dd364031c0641830c0d163bd382d7c6927e2669619ce77051f65NaN05/31/2023 05:00:00 PM15UNKNOWNUNKNOWNCLEARDAYLIGHTPARKED MOTOR VEHICLENOT DIVIDEDNaNSTRAIGHT AND LEVELDRYNO DEFECTSON SCENENO INJURY / DRIVE AWAYNaNNaNN$501 - $1,50005/31/2023 10:16:00 PMUNABLE TO DETERMINEUNABLE TO DETERMINE1515WCORNELIA AVE1922.0NaNNaNNaNNaNNaNNaN2NO INDICATION OF INJURY0.00.00.00.00.01.00.0174541.945084-87.667035POINT (-87.667034508677 41.945084209514)
8978792ee6209bde600a6ae2f12fb385b1e5749803cc01d0e954d5016091ecb13f424d48e097f71fa5d95741f5870f7d3a76d9189293c77411b3b92c925d26239872b3NaN05/29/2023 09:40:00 PM30UNKNOWNUNKNOWNCLEARDARKNESSTURNINGOTHERNaNSTRAIGHT AND LEVELDRYNO DEFECTSNaNNO INJURY / DRIVE AWAYNaNNaNYOVER $1,50005/29/2023 09:59:00 PMUNABLE TO DETERMINEUNABLE TO DETERMINE3500SWENTWORTH AVE915.0NaNNaNNaNNaNNaNNaN2NO INDICATION OF INJURY0.00.00.00.00.02.00.0212541.830922-87.631651POINT (-87.631650518377 41.830922441769)